From Medical Records to Diagnostic Dialogues: A Clinical-Grounded Approach and Dataset for Psychiatric Comorbidity
Authors: Tianxi Wan
Jiaming Luo
Siyuan Chen
Kunyao Lan
chenjianhua
Haiyang Geng
Mengyue Wu
Venue: ICLR 2026 Poster (2026)
Keywords: Psychiatric Comorbidity
Diagnostic Dialogue
EMR Dataset
Multi-Agent Simulation
Primary area: datasets and benchmarks
TL;DR: We present PsycoTalk, the first large-scale, clinically grounded dialogue dataset for psychiatric comorbidity, generated via a two-stage framework combining synthetic EMR construction and multi-agent diagnostic simulation.

Abstract: Psychiatric comorbidity is clinically significant yet challenging due to the complexity of multiple co-occurring disorders. To address this, we develop a novel approach integrating synthetic patient electronic medical record (EMR) construction and multi-agent diagnostic dialogue generation. We create 502 synthetic EMRs for common comorbid conditions using a pipeline that ensures clinical relevance and diversity. Our multi-agent framework transfers the clinical interview protocol into a hierarchical state machine and context tree, supporting over 130 diagnostic states while maintaining clinical standards. Through this rigorous process, we construct the first large-scale dialogue dataset supporting comorbidity, containing 3,000 multi-turn diagnostic dialogues validated by psychiatrists. This dataset enhances diagnostic accuracy and treatment planning, offering a valuable resource for psychiatric comorbidity research. Compared to real-world clinical transcripts, PsyCoTalk exhibits high structural and linguistic fidelity in terms of dialogue length, token distribution, and diagnostic reasoning strategies. Licensed psychiatrists confirm the realism and diagnostic validity of the dialogues. This dataset enables the development and evaluation of models capable of multi-disorder psychiatric screening in a single conversational pass.

FROM MEDICAL RECORDS TO DIAGNOSTIC DIALOGUES: A SUINICL--GRUUDDEE APPROACH AND DATASET FOR PSYCHIATRIC COMORBIDITY
Tianxi Wan', Jiaming Luo', Siyuan Chen', Kunyao Lan', Jianhua Chen?
Haiyang Geng", Mengyue Wu!*
X-LANCE Lab, Shanghai Jiao Tong University, China
2 Shanghai Mental Health Center, SJTU School of Medicine, China
3 Chen Frontier Lab for Al and Mental Health, Tianqiao and Chrissy Chen Institute, Shanghai, China
(xixi0630, 1eojm2017, lankunyao, mengyuewu) @s jsjtu. edu. cn
chensl.yuan9255outoooo... com jianhua chen smho. org. on Beeghhalyanglchennnssttttttee
ABSTRACT
Psychiatric comorbidity is clinically significant yet challenging due to the com- plexity of multiple co-occurring disorders. To address this, we develop a novel approach integrating synthetic patient electromic medical record (EMR) construc- tion and multi-agent diagnostic dialogue generation. We create 502 synthetic EMRs for common comorbid conditions using pipeline that ensures clinical relevance and diversity. Our multi-agent framework transfers the clinical inter- view protocol into hierarchical state machine and context tree, supporting over 130 diagnostic states while maintaining clinical standards. Through this rigorous process, construct PsyCoTalk, the first large-scale dialogue dataset support- ing comorbidity, containing 3,000 multi-turn diagnostic dialogues validated by psychiatrists. This dataset enhances diagnostic accuracy and treatment planning, offering a valuable resource for psychiatric comorbidity research. Compared to real-world clinical transcripts, PsyCoTalk exhibits high structural and linguistic fidelity in terms of dialogue length, token distribution, and diagnostic reasoning strategies. Licensed psychiatrists confirm the realism and diagnostic validity of the dialogues. This dataset enables the development and evaluation of models capable of multi-disorder psychiatric screening single conversational pass.
INTRODUCTION
Psychiatric disorders account for over 125 million disability-adjusted life years globally (Collab-) orators et al. (2022). A major challenge lies in psychiatric comorbidity, i.e., the co-occurrence of multiple conditions, which significantly complicates diagnosis and treatment. For instance, Netherlands study of depression and anxiety, 67% of individuals with primary depression diagno- sis had current and 75% had lifetime comorbid anxiety disorders (Lamers et al. existing datasets and models narrowly focus single disorders (Aich et al. 2024
2011). Yet. most Yao al. 2022),
while the few corpora covering multiple conditions lack fine teee-grrned annotations and fail to capture symptom co-occurrence and progression within diagnostic processes (Sun et al.) 2021 Yin al.) 2025 Cohan et al.] 20118.. As a result, large language models (LLMMs) have not been systematically evaluated on distiddisorder diagnostic tasks, limiting the development of reliable screening systems that require step-by-step reasoning grounded in DSM-5 standards (Association et al. 2013
To construct large-scale comorbidity diagnostic dialogues, diverse patient profiles reflecting real- world complexity are needed. Such dialogues are essential because they simulate step-by-step di-
"Corresponding author.
'DSM-5 is the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders by the American Psychiatric Association. It provides standardized criteria for diagnosing mental disorders and is used to ensure accuracy and consistency.

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Social Nedia Posts
PsyCoProfile
Stage1: Generation
Stage2: Diagnostic Dialogues Generation
EMR
Age:...
PsyCoTalk
Gender....
Chief Complaint:
Medical Condition:
you lototy? Anything
Gender....
Profile
Diagnosis-Agents
Profile
particutar bothering
Speciall
LLL
conversations
been still wake drained, like! didn't rest enough...
Doctor Agent
Patient Agent
Fictional
Experionce
Tool Agent
Descriptin
Interview and Diagnosis Framework
trouble focusing things outside studying
Hierarchical Diagnostic
Diagnostic Context Tree
State Machine
Figure 1: Framework overview: 1) EMR: construct patient profiles with 6 comorbidity types in
electronic medical record structure by extracting social media posts of users self-reporting multiple disorders; Dialogue: build multi-agent framework with hierarchical state machines, based SCID-5-RV (First et al. 2014), a standardized and semi-structured interview guide for major disor- ders assessment, construct nomorbidity-focuse diagnostic dialogue dataset PsyCoTalk.
agnostic reasoning and enable downstream applications such as multi-disorder screening, clinical decision support, and the training of dialogue agents for psychiatry. Yet profiles alone often lack structured detail to support accurate diagnosis. Electronic medical records (EMRs), by contrast, provide a standardized format that includes wide range of clinical data, such as symptoms and medical history, which are crucial for later training. Still, EMRs can not handle diverse and complex clinical scenarios during diagnosis and treatment planning and not capture the dynamic doc- tor-pate patient interaction, making it necessary design structured clinical dialogue flow that guides the diagnostic process and ensures realism and clinical validity.
This work aims to advance comorbidity diagnosis through the construction of PsyCoTalk, large- scale dialogue dataset designed for both data-driven modeling and systematic evaluation of diag- nostic reasoning. Our framework transforms self-reported social media posts into structured EMRs, which serve directly as patient agent profiles to drive a three-agent diagnostic system (doctor, pa- tient, tool) for generating clinically grounded multi-turn dialogues. As illustrated in Figure T introduce an innovative data construction and dialogue generation pipeline and make three key con- tributions:
We develop nuanced pipeline for EMR-driven dialogue generation that constructs synthetic, clinically grounded EMRs for comorbidity patients, offering set of 502 synthetic EMRs along with detailed personal experiences reflecting common comorbid conditions, including Depression, Anxiery, Bipolar and Artention -Defiitsn--efiitts.
We propose clinically grounded multi-agent framework combining Hierarchical Diagnostic State Machine (HDSM) and Diagnosis Context Tree (DCT) for diagnosis, inspired by clinical inter- view manuals and covering 130+ diagnostic states while following psychiatric assessment protocols.
We introduce PsyCoTalk, large-scale dataset of 3,000 multi-turn diagnostic dialogues grounded in our EMRs and validated by psychiatrists, which is the largest of its kind and features longer and more clinically deep dialogues compared existing corpora.
RELATED WORK
Mental-health dialogue corpora Recent datasets fall into groups Single-disorder resources dominate: D Yao et al. 2022) contains 1339 Chinese doctor patient dialogues on depression; doctor-patient
PsyQA (Sun et al. 2021 offers 22 000 question-answer pairs, later enlarged to 55 000 supportive
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conversations in SMILECHAT QQu et al. 2023) and EFAQA (Hai Liang Wang 2020) adds 20 200 real counselling sessions. Broader clinical sets remain rare. CED-BS (Aich et al. 2024) targets bipolar disorder and schizophrenia, while MDD-5k (Yin et al., 2025 is the largest Chinese diag- nostic corpus so far, with 5 (00 multi-turn simulations covering more than 25 disorders. Although MDD-5k uses one-to-many case-to- -oos-loodi strategy to boost diversity, it still treats each illness in isolation and offers limited control over combinatorial symptom paths.
LLM-driven dialogue simulation LLMs now power many mental-health studies. Early work assessed general chatbots: LLM-EEmoweree Chatbots (Chen et al. 2023) measured ChatGPT's di- agnostic empathy, and Patient-V V// (Wang et al.
rules with LLMs to tutor counsellors. CPsyCoun (Zhang et al. (2224) turned structured therapy notes into trainable conversations, improving topie coverage. Multi-agent designs add stronger clinical grounding. The AMC framework (Lan et al.) (024) links doctor, patient, and supervisor agents through a three-level memory, enabling domain adaptation without fine-tuning. Building on AMC, MDD-5k 5k introduced neuro-symbolic controller and dynamic diagnosis tree to steer topic flow (Yin) ct al. 2025). These ideas inform our HDSM-Agents which extends the agent paradigm with a hi- erarchical state machine and context tree explicitly aligned to authoritative interview standards and, for the first time, generates dialogues reflecting comorbid diagnostic reasoning. Yet no prior corpus simulator provides lcage-scale, clinically structured resource for psychiatric comorbidity. Our work addresses this critical gap for the first time.
2024) combined
3 PATIENT EMR AND EXPERIENCE GENERATION WORKFLOW
To create standardized EMRs for training purposes, we collaborated with psychiatrists to develop reference standard based real clinical cases. Each EMR includes seven key components: De- mographic Information, Chief Complaint, Medical Condition, Medical History, Personal History, Family History, and Preliminary Diagnosis. Mental status examination and auxiliary cluded due to the limitations of social media data. Detailed descriptions of the content structure and language requirements for each section are provided in Appendix A.3
are ex-
Life Events Classifler
evennts
Posts with Life Events
Posts
Symptom Classifler
V eympttoms
Hard Thresholding
Posts with Symptoms
Filter
Useful User
Keyword
Keyword- Basedd
Search
Classification
Context Cutting
Gender
Summary
Rule-Based
Education
Classification
LLM-Based Inference
Marital
Occupation
LLM Inference
Condition
Age
Medical History
Personal History
Family
Medical
History
Condition
Modules
Chief Complaint
Figure/ 2: Overview of the EMR generation pipeline. Starting from social media posts, different modules of the EMR are generated using distinct methods, with the overall process proceeding in top-down manner. The LLM used for EMR generation is GPT-4o-mini (Achiam et al. 2023).
3.1 EMR GENERATION
Dataset Selection We selected the PsySym (Zhang et al. 2022) dataset as our base due to its scale, depth of annotation, and suitability for modeling psychiatric comorbidity. PsySym originates from Reddit users with self-reported psychiatric disorders, offering fine- mne-grained symptom-leve an- notations across 7 major mental disorders. The dataset contains 5,624 diagnosed users, with an average of 102.5 posts per user, making it ideal for our needs.
User Filtering
To ensure meaningful interactions, filtered users based on symptom diversity
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and frequency. We retained users with at least 10 symptom seeptom-related posts and 20 distinct symp- tom types. Using a DSM-5-aligned disease- disease-symptom symptom graph, we excluded users with inconsistent symptom-label pairs, ensuring clinical reliability.
Generation Strategy We adopted a modular approach to EMR generation, which outperformed a single-step aggregation method in terms of information recall, classification accuracy, and reasoning coherence. This approach involves categorizing post content into the seven standard EMR sections, generating each section individually using either rule-based methods LLM inference, and merg- ing them into complete record. During EMR generation, we iteratively sampled cases for expert review by eight psychiatrists; their feedback guided revisions to ensure medical validity. Processing Strategies As illustrated in Figure 2 traction and generation:
we designed four strategies for information ex-
Chief Complaint and Medical Condition: Using dual classifiers for symptoms and life events, we generate binary symptom- vectors for each post, which are then summarized and inte- grated into prompts for LLM-based generation.
Medical, Personal, and Family History:
Posts categorized using dcyword-basee classifica- tion, summarized, and grouped into segments for LLM inference.
Education, Occupation, and Implicit Age: Relevant content retrieved through keyword search, trimmed with local context, and formatted into structured prompts.
Gender, Marital Status, and Explicit Age: Demographic information extracted directly using predefined keyword rules and rule-based classification.
3.2 PSYCOPROFILE DATASET DESCRIPTION AND STATISTICS
After two rounds of user filtering and using a modular strategy for content extraction and label injec- tion, we generated 502 structured EMRs from retained texts. These cover six disease combinations of four core psychiatric conditions: Major Depressive Disorder (MDD), Anxiety Disorder (AD), Bipolar Disorder (BD), and Attention- DerentionDDffiiiityypeerciit Disorder (ADHD). (Tall Issowws user counts before and after filtering, plus statistics on average posts, posts with symptoms, life events, and distinct symptoms. Results show retained users provide rich, dense, analyzable content.
Table 1: User filtering and posting statistics by disease combination. U-Ist, U-2nd: after first and second filtering; PS: symptom posts; PLE: life events; S7: symptoms.
DC
Initial
U-1st
U-2nd
Posts
PS
PLE
ST
L'AD', 'MDD'1 ] 'BD', 'MDD'
310
157
141
127
25
13
27
237
124
66
126
25
14
27
['ADHD' 'AD', "MDDD"]
194
99
92
110
12
27
('ADHD" 'MDD']
220
88
75
167
11
[ ('AD", "BD', , "MDD"]
106
74
73
110
34
19
28
[ 'ADHD', "AD'
146
69
55
163
23
11
27
Average
184
95
75
134
25
13
27
To validate the realism of the synthetic EMRs, we compared them with real-world data across disease distribution, demographic factors (age and gender), and family history. The results are illus- trated in Figure 3. Disease prevalence synthetic EMRs broadly aligns with real-world data while exhibiting more balanced distribution. Age shows peak in the 20-24 group for synthetic data versus 30-34 for real records (Collaborators al., (0022), likely reflecting the younger social me- (202)) dia demographic (DataReportal] 2020). Gender proportions more balanced in synthetic records, consistent with reduced stigma in online contexts (Portcous & Armstrong, 202I). Family history slightly overrepresented but remains within clinically plausible bounds. Psychiatrist feedback sampled records confirmed sufficient diversity and clinical plausibility, closely matching real-world documentation.
For real-world comparison, obtained approximately 1,000 de-identified clinical records from the Na- tional Mental Health Center, provided with institutional approval and used strictly for research purposes.
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pe-eg
Real Life
19-34
0.14
0.21
0.13
10-200
0.19
0.38
0.5
0.5
0.56
0.44
3.39
EMR
(a) Age
(b) Disease
Gender
Family History
Figure 3: Comparisons between synthetic EMRs and real-world data.
3.3 FICTITIOUS PATIENT EXPERIENCE GENERATION
EMRs provide structured and factual information, but they lack the narrative richness and variability found in real patient-doctor interactions. By generating personalized fictitious experiences, we can create a more comprehensive and varied dataset that includes not only the factual data from EMRs but also the nuanced, context-specific details that arise in clinical conversations. Psychiatric diag- noses primarily rely on language- based interactions, making feasible to generate multiple coher- ent diagnostic dialogues from a single EMR, provided the symptom presentation remains consistent with the diagnosis
YYi et al. 2025).
We therefore propose a personalized fictitious experience generation method grounded in struc- tured EMRs to increase the diversity and realism of simulated dialogues. Unlike MDD-5k, which randomly samples templates, our approach aligns sampled content with EMR attributes to avoid mantic confficts and incorporates clinically relevant lifestyle attributes. This ensures both semantic coherence and diagnostic validity in generated dialogues.
Concretely, we employ L.MMs to process each structured EMR via prompt-based instruction tuning. For each case, the model guided generate a total of personalized Personal Histories, includ- ing both existing entries from the EMR and newly generated ones when necessary. In addition, 10 semantically consistent Fictitious Experiences are produced, all aligned with the EMR content and free from logical conflicts. Here, Personal Histories refer to lifestyle or health background (e.g., "prefers light food, smokes and drinks occasionally, and exercises three times per week"), while Fictitious Experiences describe past events potentially affecting mental health (e.g.. "wus blamed as the main cause of a failed company project one year ago"). As shown in Figure 4, the gener- ation process outputs structured dictionaries: Dais for personal histories and Dnc for fictitious experiences. Given structured EMR input XEMR and é represents the free-text narrative description corresponding to selected fictitious experience Formally, define:
Die, D LLM (Prompt(XEMR))
LLM (LM(PPoop((h,e))
Dris:
Due
This two-stage generation mechanism enables each EMR yield up 50 unique fictitious expe- riences, thereby enhancing the diversity, flexibility, and clinical realism of downstream diagnostic dialogues while preserving semantic consistency. For detailed description of prompt please refer to Appendix A.4
4
MULTI-AGENT DIAGNOSIS FRAMEWORK
To construct clinically realistic and diverse dataset for psychiatric diagnostic dialogues, devel- oped a multi-agent framework, that integrates structured EMRs with dynamic dialogue generation. This framework comprises two main components: clinical-gruundee interview pipeline, and multi-agent execution mechanism.
4.1 CLINICAL- INTERVIEW PIPELINE
The Structured Clinical Interview for DSM-5 (SCID-5-RY is a standardized tool for assessing men- tal disorders, offering systematic approach to symptom evaluation and diagnosis. Its diagnostic
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structure has been widely validated in diverse clinical and cultural settings (Mohamaakhhani al. 2020 Shankman et al. 2018) Brodey et al. 2018). Adhering to SCID-5-RV grounds our framework restructure SCID- 5-RV into two parts: Hierarchical Diagnostie State Machine (HDSM) and a Diagnostic Context Tree (DCT). This design ensures structured, dynamic, and clinically coherent dialogue flow.
established clinical practices. To guide LLs-b psychiatric interviews,
Hierarchical Diagnostic State Machine (HDSM) The HDSM adheres to the SCID-5-RV proto- col, assigning one sub-state machine each target disorder (MDD, AD, BD, ADHD). Each sub-state machine terminates in explicit diagnostic outcomes; for MDD, the terminal states depress on 1- depres sepessioon ssion5 in Figure 5] represent five mutually exclusive diagnoses (see the complete archi- tecture in Appendix A.7p. In contrast to MDD-5k, which selected topics in random order, HDSM enables the agent to both ask questions and refine the diagnosis iteratively, reflecting real clinical reasoning. Following clinical design, the HDSM consists of a Graphical three-level hierarchy:
Interview and Diagnosis Framework
Psychiatric Diagnosis Interview
Hierarchical Diagnostic State Machine
Diagnostic Context Tree
MDD Sub-
BD Sub- Machine
ADHD Sub- Machine
AD Sub-
Family History
Personal History
Experience
State Machine
State Machine
Inquiry
Addictive Behavior History
Physical Activity Habits
Next Topic
Diagnosis Result
Receiving Dialogue History
Islia kndd
SSM OrderGan
3Pa rskkxp
Curzen Topic
Response Classilies
Fi FicExpeen
LLMeRule
Tool Agent
DocPromptGen
PromptGn
Generate
Combine
Doctor persona
Dialogue Generstion
Dialogue Generation
Fictitious Experience Destription Four years ago, I was still high school student. At that time,
PerHistDict
"I " ",Dentes smoking history "2":...
Age: 35
Gender: fomale
Prompt luning
LLM
been mostly fine lately, just occasionall irritable, but not every day.
Prompt Juning
Gen
Specialities
auxiety,
Gen
Age: [20-24] Gender: Female
FicExpDict "I "I":Cut off contact a close friend frienf
depression;
Chief Complaint:Nerroonnee and jeritabiity ...
Empathy
Strategy:
Have been feeling irvitable excited most of the day during this time?
Patient Infomation
Explamation:
Initial diagnosis
Communication
DSD-KG
Gen
Style:gentle
depression
adhd
Diogmosis speed:
slow
LLM
Symplxc
sad
anxiety
mnia
bipolar
Doctor Agent
Patient Agent
Figure 4: Multi-agent Diagnosis Framework. LLM-based doctor and patient agents interact under a tool agent that manages dialogue flow and diagnostic state transitions. The tool agent combines LLM and rule-based logic to generate patient experiences and regulate the Dynamic Diagnostic Tree.
Overarching modules (e.g., current-episode screening). shown as large hollow rectangles and represent overarching modules, such as current-episode screening (A.1) or episode history 15).
High-Level States (HLS):
Group related symptoms under HLS, displayed solid rectangles. They group related symptoms under an HLS and typically have no fixed time window. A special subset of ILS, referred as sub-state groups, is represented by dashed rectangles, and aggregates closely related questions for joint inquiry.
Lntermediate-Level States (ILS):
Basic-Level States (BLS): Terminal nodes corresponding to individual questions. mrewt drawn as solid circles. These are terminal nodes corresponding individual questions, typically constrained by time and embedded within sub-state groups. Except for the dashed sub-state groups, each ILS and BLS node corresponds specific question derived from SCID-5-RV. Questions fall into of

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four categories: (i) affective cognitive symptoms, (ii) physiological or behavioral changes, (iii) functional impairment or risk, and (iv) comorbid or contributing factors (for a full list nodes and their categories, see Appendix A.5).
Natural language cues. Within each sub-state group, only the first question adopts a precise temporal phrase such as "in the past two weeks"; subsequent questions use looser expressions like "recently" to avoid unnatural repetition.
Binary symptom scale and flow control. The original four-point SCID-3-RV symptom scale ("un- clear", "absent", "subthreshold", "threshold") is reduced to binary form: present or absent. We do not discard severity information this simplification; it is encoded in specific HDSM nodes and transitions (e.g., duration constraints such A254 > week, and functional impairment checks such as A23 on school, work, or social functioning), while the binary variables only indicate whether di- agnostic thresholds met. We adopt this binarization not because the four-level severity scale is unimportant, but because distinctions such as "below threshold" versus "insufficient information" require nuanced clinical judgment: in our early experiments, asking LLM agents maintain four- level severity ratings frequently produced inconsistent or contradictory outputs, leading to diagnostic loops or invalid transitions, whereas binary thresholding yielded more stable and auditable diag- nostic process. Colored arrows in Figure 5) illustrate how different responses guide the interview trajectory. If a BLS node has no valid successor, the agent performs a localized random traversal within the sub-state group. The transition to the next diagnostic state occurs only after all termi- nal BLS nodes (i.e., those without successors) have been visited. the number "positive" (i.e., "present") responses exceeds threshold, the group is deemed positive and follows the correspond- ing path; otherwise, absent and triggers an alternative transition.
A.1
A.I5
A97N
A97
A96N
A1N
depressiond
A96
A96Y
A1Y
A2Y
ADOY
A97Y
A3
A6
A9
APY
AB8
A114
A114Y
A16
A13
A1SY
A12
25
depression3
A:17
A17Y
A11BY
A116
A122
A12ZY
25
A27Y
A27
A24Y
A24
A23
A121
A01
depreision2
depression
Present
Present/Absent
Abscat
depression3
Figure 5: MDD sub-state machine. Solid nodes denote topic-specific questions. Colored arrows represent binary responses (present vs. absent), i.e., whether the patient exhibits the symptom. Groups A00 and A01 are activated when 2 "present" responses are observed; otherwise, they follow the alternative transition.
Diagnostic Context Tree (DCT) To further enhance the semantic depth and contextual coherence of diagnostic dialogues. we introduce the DCT, a tree-structured semantic controller that operates alongside the HDSM. The DCT designed dynamically manage the flow of the dialogue, en- suring that it remains clinically relevant and logically coherent. The top-level branches of the DCT include three main categories: Family History, Personal History, and Experience Inquiry. These categories are structured to capture the comprehensive background information necessary for thor- ough psychiatric evaluation.
The Experience Inquiry node within the DCT is dynamically triggered at the end of each turn, based on the context of the conversation. This ensures that the dialogue remains responsive and adaptive to the specific needs of the interaction. After the completion of the HDSM, the remaining lcaf nodes of the DCT are visited in a randomized order to maintain a natural flow conversation. Personal history templates are conditionally selected based on patient gender, ensuring that the dialogue is tailored to the individual patient's 's background (see Figure for male-specific examples).
By integrating the DCT with the HDSM, effectively augment the elicitation of relevant back- ground information, leading to dialogues that are more logically coherent and clinically aligned with real- world psychiatric consultations. This combination ensures that the dialogue not only fol-

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lows structured diagnostic process but also incorporates the necessary contextual details to support a comprehensive and accurate diagnosis.
4.2 MULTI-AGENT EXECUTION MECHANISM
Our simulator employs three specialized agents to facilitate each dialogue generation:
Patient Agent The patient agent formulates responses based on (i) the structured EMR, (ii) its fictitious personal experience, and (iii) the current topic provided by the tool agent. Initial experi- ments revealed bias towards affirming all symptoms, even those not documented in the EMR. To mitigate this, we developed a Disease-Sympion Description Knowledge Graph (DSD-KG) derived from SCID-5-RV guidelines. For each question, the agent consults the DSD-KG; the symptom is absent from the EMR or contradicts the provisional diagnosis, the agent responds with no. This mechanism filters out hallucinated agreements, enhancing the credibility of the dialogue.
Doctor Agent The doctor agent poses questions according to the topic queued by the tool agent. To avoid monotony, we defined five distinet doctor profiles varying in age, specialty, empathy style, verbosity, diagnostic speed, and explanation frequency. Profile cues and high-quality few-shot ex- amples are injected at prompt time, enabling the L.M to adapt its responses (e.g., concise, gentle, analytical). Reply length limits and rotating pool of empathy phrases further reduce repetitive responses.
Tool Agent The Tool Agent serves as the central controller, bridging natural-language dialogue with the symbolic diagnostic framework. Its responsibilities are divided into two main categories: tree management and dialogue coordination.
Tree Management
Sub Serb-State-oe Machine CelachinneOrdeeeeee -OrderGen (conv) + list: Determines the execution order of the four disorder-specific sub-state machines after the first dialogue turn. It supports two strategies: random shuffle symptom-informed mode, which prioritizes modules based on current symptoms.
ResponseClassi sirrrriff (conv) + bool: Classifies the patient's latest answer as present or absent and forwards this label to the HDSM to trigger state transitions.
(conv) - node: Decides whether to enter the "Experience Inquiry" branch of the context tree based the previous turn's 's content.
Dialogue Interface
Bui 1dP rompt (topic) - text: Generates aligned prompts for the doctor and patient agents based on the current topic node in the diagnostic tree, ensuring semantic consistency.
IsDial (tree) + bool: Determines whether the consultation should terminate. The dia- logue ends when all disorder-specific sub-state machines reach their terminal states and all required nodes in the context tree t the optional experience inquiry have been traversed.
FINAL PrODUCT: PSYCOTALK DATASET
Generation Process We begin with 502 structured EMRs from PsyCoProfile. For each EMR, sample five distinct fictitious experiences from the generated Dic and pair them with five dif- ferent personal histories from Duis- resulting in five Fictitious Experience Descriptions (FEDs) that capture sptcent- contextual narratives. Each FED is assigned a unique doctor profile that varies in empathy level, verbosity, and diagnostic pacing. All three agents-tth the doctor, patient, and tool agent-an are are instantiated using the Qwen2.5-72B model (Bai et al.] (023). deployed locally on server with four NVIDIA A100-SXM4-806K
GPUs using vLLM (K et al. 2023). For each FED and doctor profile pair, we generate two dialogues using different sub-state machine scheduling strategies: one that is symptom-informet and another that uses randomly determined order. This process yields approximately 5,000 dialogues. From these, we select 3,000 dialogues whose final diagnostic labels match of the six predefined comorbidity combinations in PsyCoProfile to form the final Psy Calk dataset. As all human evaluators are native Chinese speakers, we release only the Chinese version ensure annotation quality. For comparative analysis, include two addi-
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tional Chinese diagnostic datasets, D and MDD-5k, along with de-identified clinical dialogue set RRell-oordd Dial) obtained from psychiatric hospital. Dataset statistics are reported in Table
Table 2: Statisties of different datasets. Avg. chars (D) and Avg. chars (P) measure the average Chinese characters per doctor's 's response and patient's 's response.
Avg. chars
Avg. chars (P)
Avg. Turns
Dataset
#Disorders Comorbidity #Dialogues
(D)
Real-World Dial
28.3
35.8
x
D
20.4
14.9
21.6
Depression
X 1,339
MDD-Sk
91.1
162.8
26.8
>25
X
5,000
PsyCoTalk
34.0
43,5
45.9
4
/
3,000
Evaluation We conduct two types of human evaluation. First, 50 dialogues from PsyCoTalk are rated in double-blind fashion by five licensed psychiatrists with more than seven years of clinical experience (randomly drawn from 3,000 dialogues, i.e., 1.67% of the corpus, with diagnostic and demographic ratios closely matching the full dataset: depression 0.35 vs. 0.38. anxiety 0.32 vs. 0.31, ADHD 0.21 vs. 0. 19, bipolar 0.12 vs. 0.12, female 0.53 vs. 0.50; this evaluation scale is comparable or more rigorous than prior clinician- based mental health LLM studies, which typically assess ~0.1 10.155 of their corpora with 2-5 expert raters Qi et al. (202)) Yin et al. (2025): Gabriel et al.)
2024): Yang et al. (2024)). Each dialogue evaluated across six dimensions grouped under four criteria: Professionallimm, Communication, Fluency, and Realism. Professionalism assesses whether the doctor successfully elicits all symptoms necessary for diagnosis. Communication evaluates (i) the doctor's 'ss proactivity in questioning and (ii) the patient's responsiveness. Fluency measures (i) syntactic and topical coherence and (ii) avoidance of redundancy. Realism assesses how closely the dialogue resembles actual psychiatric consultations. Second, we perform AB test using 10 two-turn excerpts sampled from each dataset (PsyCoTalk, D MDD-5k, and world Dial). The same five psychiatrists are asked judge whether each excerpt appears real or Al-generated. Each excerpt identified as "real" receives one point, and final realism scores are normalized to a 10-point scale.
Objective Comparison As shown in Table 2|
PsyCoTalk excels in scale and structural fidelity. the only dataset for psychiatric comorbidity, with 3,000 dialogues-over twice D+ and close to MDD-5k. Each conversation averages 45.9 turns, nearly double other corpora. Utterance lengths are 34.0 characters (doctors) and 43.5 (patients), closest real clinical conversations (28.3 and 35.8). By contrast, MDD-5k deviates most, with overly long utterances that reduce realism.
To verify the effectiveness of the fictitious personal experience module under EMR reuse and to validate the cross-lingual generalization of our pipeline, we assess dialogue diversity and conduct a small-scale English generation experiment compared with existing corpora; detailed metrics and results reported in Appendix A.2 A.2
To assess diagnostic accuracy, we use initial EMR labels as ground truth and evaluate with exact- match (all disorders correct). A zero-shot Qwen2.5-72B baseline reaches 0.22 subset accuracy, while our HDSM-guided multi-agent system improves to 0.31, significant gain (McNemar's test, p= 7 x 10 < 0.001). On 200 sampled cases, GPT-4o-mini and Deepseek-v3 achieve below 0.1, Qwen3-32B below 0.02, and Qwen3-8B below 0.04, Per-label Fl: MDD (0.92), AD (0.81), ADHD (0.64), BD (0.40). These align with clinical trends where BD and ADHD are harder to diagnose than MDD and AD due to symptom overlap, subtler onset, and higher heterogeneity (Hui et al. 2018
Barkley & Brown 2008
Table 3: PsyCoTalk 6-dimension evaluation. (Prof. = pro- fessionalism: Coom..i)/(n = communication; Flu.(i/(i)) Table 4: AB-test results. (Real real-world fluency; Sim. similarity)
pro-
dial)
Prof. Comm.(i) Ciim. Flu.(i)
Flu.(ii)
Sim.
Dataset
Real
D*
MDD-Sk
PsyCoTalk
7.72
8.14
8.24
7.42
6.79
6.67
Score 6
4
Subjective Results
Table summarizes expert evaluation results across six dimensions. Psy- CoTalk ranks highest in communication (8.14 for doctor initiative, 8.24 for patient engagement),

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alongside strong scores in professionalism (7.72) and fluency (7.42 and 6.79), reflecting coherent and context-aware interactions. In the AB test for perceived realism. Table 4] shows that PsyCoTalk achieves score of 5, second only to real-world data (6), indicating that its dialogue style closely ap- proximates clinical expectations. By contrast, MDD-5k scores the lowest (1) due to its templated and repetitive utterances, which reduce perceived authenticity. These findings confirm that our HDSMM based multi-agent framework produces dialogues that are not only diagnostically informative but also linguistically coherent and clinically credible across diverse expert judgments.
LIMITATIONS
PsyCoTalk focuses on four prevalent psychiatric disorders and their comorbidities, which reflects common real-world cases but limits coverage of rarer conditions. This choice is driven by the scarcity of clinically reliable comorbidity data, we retain only combinations with sufficient symp- tom diversity and sample size to support stable EMR construction. While we conducted a small- scale English generation experiment, the main dataset remains Chinese due to expert-evaluation constraints, restricting multilingual applicability. Moreover, Reddit-derived symptom cues may in- troduce demographic cultural skew, although the EMR template and SCID---based HDSM language- and culture-agnostic and can be ported to local clinical corpora. Nevertheless, our pipeline extensible and can be scaled to broader disorder coverage and coos--lnngual settings. Future work will conduct more experiments and evaluations to quantify the dataset's 's impact on model perfor- mance and diagnostic reasoning.
CONCLUSION
In this paper, we introduce two-stage pipeline that creates the first large-scale, clinically standard dataset for psychiatric comorbidity. First, we develop PsyCoProfile, converting social media posts into 502 structured electronic medical records that reflect real-world prevalence of six common disorder combinations. Second, we propose a multi-agent interview and diagnosis framework that transforms these records into PsyCoTalk,, corpus of 3,000 multi-turn dialogues mimicking clinical interviews. PsyCoTalk provides the necessary scale and detail to train models for multi-disorder screening.
ETHICS STATEMENT
The authors have read and adhere to the ICLR Code of Ethics. This work does not involve human subjects, identifiable private data, or harmful applications. No real patient data are used; the only human involvement is expert evaluation by five licensed psychiatrists, who provided informed con- sent and were compensated according to essposible-ressaree guidelines. No external sponsorship or conflict of interest influenced the design conclusions of this work.
Data provenance and privacy. The raw posts that seed Psy CoProfile are drawn from the publicly available PsySym corpus, whose collection 12022158P) and complies with the Personal Information Protection Law of the People's Republic of China. All posts are anonymous, stripped of usernames, time stamps and locations, and stored under random identifiers. Our pipeline rewrites each post into a synthetic electronic medical record; no verbatim text that could re-identify an author is retained. The fictitious personal histories and life events are generated entirely by large language models. Five licensed psychiatrists reviewed random samples and confirmed that no record contains protected health information or disallowed content. The final dialogues in PsyCo Talk are synthetic and will be released only under data-usage agreement that forbids attempts at re-identification or clinical deployment.
approved by institutional review hoard (IRB
Intended use and risk mitigation. The dataset is designed for research on multi-disorder screening and dialogue modelling. is not diagnostic tool, nor does it provide treatment advice or actionable clinical guidance. To prevent misuse, we define compliant use boundaries follows: (1) Research- only restriction: all EMRs and dialogues are synthetic and must not be used for individual clinical decisions. real-world psychological counseling, any form of patient-facing facing service; (2) Data- usage agreement: access requires signing an agreement that prohibits clinical deployment, real-user psychological services, and any re-identification attempts. Any real-world mental-health use of
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models trained on this dataset would require formal ethics review and clinician oversight. Synthetic conversations may still reflect biases inherited from web data and large language models; users must apply rigorous evaluation before any downstream the loop when analysing sensitive mental-health text, and to follow relevant professional. legal and ethical guidelines. Future work will extend coverage while continuing consult ethics boards and domain experts.
We urge practitionets to keep humans in
Societal impact. This work has the potential positively contribute to early-stage screening re- search and the development of more inclusive diagnostic tools for psychiatric profiles, particularly in settings with limited clinical resources. However, the release of realistic synthetic dialogues and records also raises risks of misuse, such as inappropriate clinical adoption or amplification of model biases in downstream systems. To mitigate these risks, we emphasize that the dataset is strictly intended for research use and should be paired with responsible modeling and evaluation practices. Broader impacts will be monitored in future iterations in collaboration with ethics committees and clinical stakeholders.
Annotator Compensation. We ensured fair compensation for all annotators. They were compen- sated at a rate of $25/hour. This rate is above the local minimum wage and aligns with guidelines for ethical crowdsourcing practices. Annotators were briefed about the nature the task, estimated time per task, and payment prior to participation.
ACKNOWLEDGEMENT
This work has been supported by the China NSFC Project (No. 62572320), the Shanghai Shenkang Hospital Development Center Project (Grant No. SHDC120251181 Tianqiao and Chrissy Chen Institute (TCCI) and EverMind AI, Inc (Program: Mental Health Data Synthesis and Scientific Data Competition).
REPRODUCIBILITY STATEMENT
We have made all code and datasets used in this work publicly available in an anonymous repos- itory to ensure full reproducibility of our experiments: https://gitthbb com/ LANCE/ PsyCo Diagnosis
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A APPENDIX
A.1 USE OF LLMS
LLMs were primarily employed for data generation, forming the basis of our multi-agent architec- ture. For the writing of this paper, note that LLMs were used only to aid polish the text, for example in grammar checking and improving readability. They were not involved in generating research ideas analyzing results.
A.2 DIALOGUE DIVERSITY AND CROSS-LINGUAL ANALYSIS
A.2.1 INTRA-EMR DIVERSITY
To address concerns about EMR reuse, enrich each dialogue with varied backgrounds while retaining symptom consistency. We measure intra-EMR diversity by 1 J(EMR,), where
2
K n Kr
J(EMR) n(n
K
with K2, K2 denoting keyword sets from dialogues generated under the same EMR The average diversity score is 0.647 0.030, i... only 35.3% overlap, demonstrating high variability among dialogues derived from the same case.
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A.2.2 COMPARISON WITH REAL CLINICAL DIALOGUES
We further compare synthetic (PsyCoTalk) and real-world clinical dialogues (1,731 patients, with 40.7% comorbid cases) using three diversity metrics:
1. Normalized Entropy (XX)/log2(V) where (X) is Shannon entropy and is vocab- ulary size.
2. Hapax Proportion Nnapaa /V, where Mapux = Hw; e V [(w:) 1
3. Semantic Diversity MeanCosSim, with MeanCosSim m=(n-1) Σis cos(ve, Vy), where each session 5; is represented by an embedding vector V4.
Table shows that PsyCoTalk (Chinese) closely matches the diversity of real clinical dialogues, while PsyCoTalk-eng exhibits comparable or even higher diversity than the AlexanderStreet dataset.
Table 5: Diversity metrics across synthetic and real dialogue datasets.
PsyCoTalk (Chinese, 3k)
Real Clinical (Chinese, 1.7k)
PsyCoTalk-eng (205)
AlexanderStreet (English, 1.3k)
Metric
Normalized Entropy
0.5974
0.5880
0.6938
0.5722
Hapax Proportion
0.3231
0.4195
0.2189
0.3143
Semantic Diversity
0.7938
0.8663
0.9746
0.9678
A.2.3 ENGLISH DATA AND CROSS-LINGUAL COMPARISON
Using the same pipeline, we construct a preliminary English dataset (PsyCofalk-enng. 205 dialogues from 41 patients) and compare it with the AlexanderStreet dataset (Alex, 1,254 dialogues without diagnostic labels). As shown in Table 5] PsyCoTalk-eng achieves higher normalized entropy and semantic diversity than Alex, while Chinese PsyCoTalk exhibits a higher Hapax proportion but lower semantic diversity than its English counterpart. This discrepancy may be attributed to language structure: Chinese tends use more unique or fow-frequency words, while English dialogues often display greater semantic variation.
A.2.4 WORD FREQUENCY AND SART-OF-SPEERE COMPARISON
We compare word frequency distributions in 200 sampled English and 200 Chinese dialogues, to- kenized by NLTK (English) and jieba (Chinese). The top 100 high-frequency words (frequency > 100) are categorized into four groups, and the results are summarized in Table 6) Here, High-freq Count (%) refers to the number and proportion of distinct word types from each category that ap- pear among the top 100 (e.g.. 28 Chinese words fall into "Symptoms/States"" accounting for 35%). Total Freq (%) denotes cumulative frequency and proportion of words that category within the sampled dialogues (e.g., Chinese symptom/state words occur 27.342 times in total, 21.88% of all top-100 occurrences). This dual perspective captures both the lexical coverage (Count) and the usage weight (Total Freq) of different categories across languages.
Table Word frequency comparison across Chinese and English dialogues.
Chinese High-freq Count (%)
English High-freq
Chinese Total Freq (%)
English Total Freq (%)
Word Class
Count (%)
Sytptoms/States
28 (35%)
26 (26%)
27,342 (21.88%)
39,553 (21.45%)
Descriptive Words
39 (48.8%)
62 (62%)
49,305 (39.45%)
113,670 (61 (6466)))
Time
19 (23.8%)
(9%)
30,702 (24.57%)
20,895 (11.33%)
Degree
14 (17.5%)
3 (3%)
17.632 (14.11%)
10,310 (5.59%)
Although distributional differences exist, both languages are dominated by descriptive and symptom-related words, with similar category rankings. Figure 6) illustrates representative word
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clouds for the "symptom/state" category in both languages, showing numerous synonymous mantically close pairs (e.g,, anxious/$0 mind/ +)) English words exhibit a more even distribution, potentially due to linguistic structural differences.
Wake Falling
(anxious)
JAFE (fail asloep! i idon' tweet to)
Distracted
A
Appetite
Frustrating Weight
HE HETE-E (unmotivated) B (thought)
cubp
B 8---1 fin the heed! 1872
Energy
Tired
7 &h (can' tleep)
(tire]
Eal Ealing
Mind
Shake
Sit
Thoughts
VOES (state)
atttetton
2 lget
94&8 linterest)
98.99 (unwell)
(strees)
HE jexcited)
Fall
Reslless
Thinking
L (in the mind) 3
(feel)
Anxious
Bed Sleep Drained
EILE (in bed]
E (down)
ME- (restless)
BERR (oossing and turning)
c (concentration)
Anxiety
Overwhelming
(distracted)
Figure 6: Word clouds of top "symptom/state" high- feeuunryyynncy words in English (left) and Chinese (right).
A.3 EMR SECTION GUIDELINES
This appendix provides detailed specifications for each section in the standardized EMR template developed this study. The content structure was developed based on standard clinical case tem- plate obtained directly from practicing psychiatrists. This template reflects real-world documenta- tion practices used in psychiatric settings and was adopted ensure clinical realism and consistency.
Demographic Information should include the patient's gender, age, educational background, mari- tal status, and occupation. The language should be concise and factual.
Chief Complaint describes the primary psychological symptoms reported by the patient, along with their duration. The text should be brief, focused, and clinically relevant.
Medical Condition covers the current psychiatric presentation in more detail. This includes the onset and course symptoms, any identifiable triggers, and the impact on the patient's 's daily functioning. General physical health indicators such sleep patterns, appetite, and weight changes should also be summarized when available.
Medical History lists prior medical conditions including chronic discases, previous hospitalizations, surgical procedures, known allergies, and history of infections. This section helps assess comorbid risks.
Personal History records health-related lifestyle factors such as smoking, alcohol use, and drug consumption. In female patients, it may also include relevant information menstruation.
Family History identifics psychiatric or neurological disorders in close biological relatives (c.g., par- ents, siblings). Information should be structured to reflect genetic predisposition or family dynamics relevant to diagnosis.
Preliminary Diagnosis provides initial clinical impression based on the information presented in the EMR. This diagnosis is tentative and may refer to DSM-based categories such as MDD, BD, or ADHD, depending on symptom patterns.
A.4 PROMPT DETAILS FOR VIRTUAL EXPERIENCE GENERATION
To enrich patient narratives with personalized and semantically coherent context, we design a struc- tured prompt-based mechanism for generating Fictitious Experience Descriptions. The generation executed by the Tool Agent using an instruction- folllwiin large language model.
The input to this process is template sentence derived from each patient's 's structured EMR, format- ted as: "I am a fagel-year-ole (gender) with (diagnosis). currently working or studying as a foccu- pation). Past experience: (experience]" The fields (age), (gender), [diagnosis), and (occupation]
15

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directly drawn from the EMR, while /experience) is sampled from the fictitious experience dic- tionary Dic.
The prompt guides the model craft first-person narrative that claborates on the given background, using the following system message:
"You are powerful, imaginative text assistant skilled writing stories. Given keywords describing a patient's 's mental health background and experiences, imag- ine and write detailed, first-person account of the patient's 's past. Expand the causes, progression, and consequences of a psychologically impactful event. Do not metaphors, exaggerated language, or fictitious names. Avoid therapeutic expressions like "under the doctor's guidance' or "trying to get better." The output should be a coherent and realistic narrative."
An example output is shown below
Four years ago, I was 12 12-year-old middle school student. I was cheerful by na- ture, but often had trouble focusing. During class, I couldn's 't help but look around, and my hands were constantly doodling on paper. The teacher was very displeased and reminded me multiple times to pay attention, but it barely worked. One day in math class, the teacher was explaining a complicated geometry problem. I had stayed up late the night before playing video games, and couldn't it follow the les- at all. My attention drifted, and the teacher's 's voice gradually became blur. Suddenly, the teacher raised his voice, "You! Stand up!" was startled, jumped to my feet, heart pounding, and my face flushed with embarrassment. He stared at me sternly and said in front of the entire class, "You" 'r not listening again, are you? With that kind of attitude, how do you expect succeed? Do you think your parents would be proud of this?" The room fell silens. All eyes were on me. felt utterly humiliated, and tears welled up in my eyes. The teacher went on: "If you don't take this seriously, you'l 'Il never amount to anything." " His words pierced through me like needles. felt defeated and deeply ashamed. After class, students whispered among themselves-See some mocked me, others looked at me with pity. I locked myself in the bathroom and cried uncontrollably. For a while after that, began to fear going to school. Just entering the classroom would make my palms sweat, and couldn't concentrate at all. My grades plummeted. My parents, anx- ious and disappointed, constantly scolded me for not studying properly. A few months later, was diagnosed with depression and ADHD. The doctor said that the public criticism had dealt severe psychological blow, and combined with academic pressure and high family expectations, it ultimately triggered the onset of my condition.
This prompt design ensures that generated Fictitious Experience Descriptions are aligned with EMR content, emotionally rich, and free from contradictions. It balances creative storytelling with medi- cal plausibility, enabling the system to simulate realistic psychological histories without relying on deterministic templates.
A.5 HIERARCHICAL DIAGNOSTIC STATE TAXONOMY AND TOPIC MAPPING
This appendix collates the full hierarchy of the HDSM, aligning each Basic-, Intermediate-, and High-Level State with its corresponding clinical topic. States are grouped according to the four SCID-5-derived categories:
(i) Affective & Cognitive Symptoms, (ii) Physiological & Behavioral Changes, (iii) Functional Im- pairment& & Risk, and (iv) Comorbid or Contributing Factors. For completeness, terminal nodes that represent each sub-stad machine's 's final diagnostic outcomes are also listed. The lists that follow serve as a reference for all experiments, ensuring that every state's semantic scope and its precise role in the dialogue-generation pipeline-is clearly documented.
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I. MDD Sub-State Machine (including HBL A.1, A.15)
A.1: Current Majoo-Deprressvv-EEpisooe Screening
Full Topic Inventory of the HBL A.1 Categorized by Four Clinical Dimensions
(i) Affective Cognitive Symptoms
A1: Depressed mood
AIN: Sadness / emptiness / hopelessness
ATY: Duration 2 2 weeks A2Y: Recent loss of interest / pleasure
A2N: Reduced interest pleasure
A2_1: Duration 2 2 weeks
(ii) Physiological & Behavioral Changes
A3: Appetite / weight change
A6: Sleep disturbance
A9: Psychomotor retardation
agiiatiin
A9Y: Observable severity
A12: Fatigue / loss of energy A11: Worthlessness excessive guilt
A13Y: Functional limitation
A16: Poor concentration indecisiveness
(iii) Functional Impairment & Risk
A17: Suicidal ideation
A17Y: Suicide plan behavior
A23: Functional impairment
(iv) Comorbid or Contributing Factors
A24: History of medical illness
A224:: Related to medical illness
A27: History of substance use
A27Y: Substance-related
A.15: History of Major-Depressive Episodes
Full Topic Inventory the HBL A.15 Categorized by Three Clinical Dimensions
(i) Affective & Cognitive Symptoms
A9:: Past depressed mood A96N; Past sadness / emptiness hopelessness
A96Y: Duration 2 weeks . A97Y: Time-specific loss of interest / pleasure
A97N: Past loss of interest pleasure A99_ Duration 2 weeks
(ii) Physiological & Behavioral Changes
A98: Appetite / weight change
AA11: Sleep disturbance
A 14: Psychomotor retardation or agitation A114Y:
Observable severity A1188 Worthlessness / excessive guilt
A 1: Fatigue / loss of energy
A118Y: Functional limitation
A121: Poor concentration indecisiveness
(iii) Functional Impairment & Risk
A122: Suicidal ideation
A122Y: Suicide plan or behavior
Final-State
Diagnosis Description
depressionl
Major depressive episode due to physical illness
depression2
Major depressive episode induced by substances medication
depression3
Primary major depressive episode
depression4 No major depressive disorder
depression5
Past major depressive episode
II. BD Sub-State Machine (ineluding HBL A.23, A.43, D.1)
A.23: Current Manic/Hyyomanic Episode
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Full Topic Inventory the HBL A.23 Categorized by Four Clinical Dimensions
(i) Affective Cognitive Symptoms
A134: Elevated manic mood
A136: Irritability
A135: Beyond normal range
A137: Duration 2 week
A137N: Irritability week
A137Y: Symptoms nearly every day
A02Y (Sub-state A02): Worst week of episode
A138 (Sub-state A02): Grandios-
ity / inflated self-esteem
A141 (Sub-state A02): Flight of ideas
A142 (Sub-state A02): Distractibility
(ii) Physiological & Behavioral Changes
A135 1: Excessive energy increased activity
A 1:36Y: Euphoria high energy
A139 (Sub-state A02): Decreased need for sleep
A.44 (Sub-state A02): Talkative-
ness
A143 (Sub-state A02): Increased work drive involvement
A144 (Sub-state A02):
Increased social activity
A145 (Sub-state A02): Increased sexual activity
A14 (Sub-state A02); Impulsive
behavior
A147 (Sub-state A02):
Restlessness
(iii) Functional Impairment & Risk
A148: Functional impairment
(iv) Comorbid or Contributing Factors
A151: History of medical illness
A151Y: Related to medical illness
A154: History of substance use
A154Y: Substance-related
A.43: History of Manic/Hypomanic Episodes
Full Topic Inventory of the HBI A.43 Categorized by Two Clinical Dimensions
(i) Affective & Cognitive Symptoms
A251: Past elevated / manic mood
A255: Past irritability
A252: Beyond normal range
A225: Duration week
A253N: Irritability 2 week
A254Y: Symptoms nearly every day
A255 (Sub-state A03): Grandiosity inflated self-esteem
A258 (Sub-state A03):
Flight of ideas
A259 (Sub-state A03): Distractibility
A286 (Sub-state A04): Grandiosity in-
flated self-esteem
A289 (Sub-state A04): Flight of ideas
A290 (Sub-state A04): Distractibility
(ii) Physiological & Behavioral Changes
A252 Excessive energy / increased activity
A253Y: Euphoria high energy
A256 (Sub-state A03): Decreased need for sleep
A257 (Sub-state A03): Talkative-
ness
A260 (Sub-state A03): Increased work drive involvement
A261 (Sub-state A03):
Increased social activity
A262 statestate A03): Increased sexual activity
A263 (Sub-state A03): Impulsive
behavior
A264 (Sub-state A03): Restlessness
A287 (Sub-state A04); Decreased need for
sleep
A288 (Sub-state A04): Talkativeness
A291 (Sub-state A04): Increased work drive
involvement
A292 (Sub-state A04): Increased social activity
A293 (Sub-state A04); Increased
sexual activity
A294 (Sub-state A04): Impulsive behavior
A295 (Sub-state A04): Restlessness
D.I: Determinative Clauses for Bipolar Diagnosis
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Label
Description
D3
At least one manic episode
D5
At least one hypomanic and one major-depressive episode
Final-State
Diagnosis Description
bipolarl
Hypomanic episode
bipolar2
Manic episode due to physical illness
bipolar3
Manic episode induced by substances or medication
bipolar4
Primary manic episode
bipolar5
No manic or hypomanic disorder
bipolarr
Past manic episode
bipolar7
Past hypomanic episode
bipolarr Bipolar I disorder
bipolar9
Bipolar disorder
III. AD State Machine (including HBL F.25, E.31)
E.25: Current -oteraaiiee-Axiityy----e Symptoms
Full Topic Inventory of the HBL F.25 Categorized by Four Clinical Dimensions
(i) Affective & Cognitive Symptoms
F140: Persistent anxiety/ worry
F112_1: Unfounded anxiety / excessive worry
F142_2: Anxiety present most of 2 6 months F1143 Difficulty controlling worry
(ii) Physiological & Behavioral Changes
F144 (Sub-state FOO): Restlessness / on edge F145 (Sub-state FOO): Easy fatigability
F146 (Sub-state FOO): Difficulty concentrating / mind going blank F147 (Sub-state
FO0): Irritability
F148 (Sub-state FOO): Muscle tension
F149 (Sub-state FOO): Sleep disturbance /
tiredness
(iii) Functional Impairment & Risk
F151: Functional impairment (study, work, social)
F163: Panic attacks
(iv) Comorbid or Contributing Factors
F152: History of medical illness F152Y: Related to medical illness
F156: History of substance/meddiatioon use
F156Y: Substance-related
F31: History of Generalized- Disorder
Full Topic Inventory the HBL F.31 Categorized by Two Clinical Dimensions
(i) Affective Cognitive Symptoms
F165: Months-long anxiety / worry
-1167 1: Unfounded anxiety / excessive worry
F167_2: Duration 2 6 months
F1668 Difficulty controlling worry
(ii) Physiological & Behavioral Changes
F169 (Sub-state FO1): Restlessness / edge F170 (Sub-state FO1): Easy fatigability
F171 (Sub-state FO1): Difficulty concentrating mind going blank
F172 (Sub-state
FO1): Irritability
Continued on next page
19

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Continued from previous page
F173 (Sub-state F01): Muscle tension tiredness
F174 (Sub-state F01): Sleep disturbance
Final-State
Diagnosis Description
anxietyl
Generalized anxiety disorder (GAD) due physical illness
anxiety2
GAD induced by substances or medication
anxiety3
Current GAD with panic attacks
anxiety4
Current generalized anxicty disorder
anxiety5
No generalized anxiety disorder
anxiety6
Past generalized anxiety disorder
IV. ADHD State Machine (including HBL K.1)
K.1: Comprehensive ADHD Symptom Assessment
Full Topic Inventory of the HBL K.1 Categorized by Three Clinical Dimensions
(i) Affective Cognitive Symptoms
K2: Easily distracted disorganized
K6 (Sub-state KO0); Frequently misses details
careless work
K6N (Sub-state KO0): Careless mistakes (e.g., billing)
K7 (Sub-state K00): Diffi-
culty sustaining attention (reading, conversations, chores)
K8 (Sub-state KO0): Seems not to listen when spoken Frequency of inattention
-8Y (Sub-state KO0):
K9 (Sub-state KO0): Leaves tasks unfinished due distraction
K12 (Sub-state
KO0): Often loses/misplaces items
K13 (Sub-state K00): Easily distracted by external stimuli
K13N (Sub-state K00):
Distracted by unrelated thoughts
K14 (Sub-state KO0): Forgetful (e.g., returning calls, paying bills)
K1l (Sub-state
K00): Avoids tasks requiring sustained attention
(ii) Physiological & Behavioral Changes
K2N: Difficulty sitting still / waiting lines required to sit
K16 (Sub-state K01): Restless when
K17 (Sub-state KO1): Leaves seat (class, cinema) frequently
K18 (Sub-state KO1);
Feeling restless when inactive
K19 (Sub-state K01): Cannot engage quietly in leisure
K19N (Sub-state K01):
Talkative/noisy when should be quiet
K20 (Sub-state K01): "On the go"; exhausting for others
K21 (Sub-state K01);
Talks excessively complaints of verbosity
K22 (Sub-state K01): Often blurts out answers
K23 (Sub-state K01): Difficulty
waiting one's turn
K24 (Sub-state K01): Interrupts intrudes others
(iii) Functional Impairment & Risk
K10 (Sub-state K00): Difficulty organizing tasks at home or work
K1ON (Sub-
state KO0): Extremely messy desk closet
K5: Symptoms persisting in the past 6 months
20

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Final-State
Diagnosis Description
adhdl
No auteetiion-dfficccyyeeeeetttt disorder (ADHD)
adhd2
Attention-dffficchyyeeerttttty disorder
A.6 CASE STUDY PSYCOPROFILE
To aid interpretability, we provide an English translation of structured EMR originally generated in Chinese. As shown in Fiiuure 7] the record describes a 20-24-yeaa ---ar--o unmarried female student with history of major depressive disorder and generalized anxiety.
The chief complaint includes emotional instability, fatigue, and occasional negative thoughts. Her medical condition outlines symptom progression over 28 months, including episodes of social with- drawal, panic attacks, sleep disturbances, and compulsive behavior, with gradual improvement fol- lowing psychological intervention. Her past medical history includes SSRI treatment, hospitaliza- tion, and CBT, well as group-based mindfulness therapy and weekly psychiatric supervision.
Her family history positive for bipolar disorder (mother) and antisocial personality disorder (fa- ther). Personal history reports menstrual irregularities, no smoking, regular caffeine intake, and preference for outdoor exercise. Demographics indicate a university education and no history of marriage. The preliminary diagnosis is depression and anxiety.
Gender: Female
Age:|20-24]
Marital Condition:
Occupation:
Education:
Currently
Unmarried
Student
pursuing bachelor's degree
Chief Complaint: Anxious and irritable, with marked mood fluctuations and occasional negative thoughts; fatigued and lacking energy. The total course of illness is 28 months, and over the past month self-esteem and sense well-being have shown some improvement.
Medical Condition: 24 months ago: extreme anxiety and depression with severe social avoidance and suicidal ideation (=3 months); 12 months ago: intensified sadness and loneliness at university (5 months) with insomnia, distractibility and intermittent panic; months ago: acute social anxiety with palpitations and tremors (6 weeks); months ago: mood swings halved after intervention but still low-mood episodes/week (6-8 each);; months ago: addictive impulses once/week; one month ago: negative thoughts now 2-3/month (< 20 min each).
4 4-y/day
Medical History:25 months ago: diagnosed with death anxiety, SSRIs months; 24 months ago: week hospitalization for depression; 18 months ago: 12-session CBT; months ago: diagnosed with recurrent depression and GAD, treated with mirtazapine & sertraline; 4 months ago: 8-week MBSR and weekly supervision.
Personal History: History of menstrual irregularities; over the past months eycles have shortened to 20-25 days with increased bleeding; denies smoking but habitually drinks one cup of coffee daily, especially in the morning; prefers outdoor activities, engaging in hiking or cyeling at least once week for about 2-3 hours.
Family History: positive: mother- bipolar affective disorder; antisocial personality disorder.
Preliminary Diagnosis:
Depression, Anxiety Disorder
Figure 7: Example of Structured EMR (English Version))
A.7 HDSM FRAMEWORK
Figure presents the full structure of the Hierarchical Diagnostic State Machine (HDSM), covering all four targeted psychiatric disorders. Each disorder is assigned independent sub-state machine, with corresponding high-level states (HLS) shown in the figure: A.1 and A.15 for MDD, F.25 and F31 for AD, A. A.43, and D.1 for BD, and K.I for ADHD.
21

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Each HLS contains two sub-state groups (dashed rectangles), except D.1, which serves a summary node in the BD sub-state machine. Sub-state groups are designed based on SCID- 5-RV item clusters, preserving clinical logic and content alignment. Each sub-state group has predefined threshold, typically 3 or 5, for determining whether the collected responses support a positive diagnostic transition. This design enables the system emulate real-world psychiatric evaluations with high clinical fidelity.
Terminal states, such depressionl through depressions in the MDD branch, represent mutually exclusive diagnostic outcomes based on structured symptom elicitation. All state transi- tions, node categories, and thresholds strictly follow the SCID-5-RV guidelines, ensuring consis- tency between the simulated dialogue flow and professional diagnostic protocols.
22

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A2N
A2 1
A.I
A.15
A97N
A96N
A1N
depression
A96
A96Y
A1
A1Y
AODY
A97Y
A3
A6
A9
A9Y
A98
A114
A114Y
A16
A13
A13Y
A12
25
depression3
A17Y
A111
A118
A118Y
A115
A122
A122Y
25
A27Y
A24Y
A23
A121
depression2
depression
Present
Absent Present/Abeen
depression5
F140
F165
anxieeyy
F142 1
F142 2
F143
F167 1
F167_2 2
F168
F172
F174
F170
anxlety3
F171
F174
F169
F00
F172
F174
F170
F171
F174
F163
F156
F152
F156Y
F152Y
F151
anxictyó
auxictyy
E.25
anxicty2
anxictyl
F.31
A251
A252
A252
A254
A137N
A136
A136Y
A253Y
A137
A137Y
A253
A254Y
A134
A135
A135
A253N
A02Y
bipolar
bipolarl
A138
A14 A146
A143
A261
A256
bipalar3
A1<A139
A261
A258
A257
A263
A264
A258
A154Y
A148
A263
A264
A255
A151Y
1
A255
A258
A259
A260
A04
A.23
bipolar
A154
bipolar2
A151
bipolarb
A259
A260
A03
bipolar
23
A.43
K2N
adhdl
D3
K2
K5
A7
KBN
A21
D5
Present
K11
K8
K8Y
K12
K19
K19N
Absent
Present/Absent
K14
K13
K13N
K00
K20
KO1
bipolur9
bipolar8
K.1
adhdde
D.1
Figure 8: Overview of the HDSM. High-level states A.1 and A.15 correspond to the MDD sub- state machine; F.25 and F.31 to the AD sub-state machine; A. A.43, and D. to the BD sub-state machine; and K.I to the ADHD sub-state machine.
A.8 CASE STUDY OF PSYCOTALK
To illustrate the structure and quality of PsyCoTalk, present an example doctor-patient dialogue in both Chinese (original) and English (translated) versions. These dialogues are generated by multi- agent interaction under the Diagnosis-Agents framework, where the patient agent is instantiated with a personalized EMR and fictitious experience, and the doctor agent is guided by a structured diagnostic state machine.
23

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The dialogue includes complete diagnostic process, from initial inquiry to the final diagnosis issued by the doctor agent. We present below an English translation of the original dialogues for illustrative purposes (see Figure 9
Figure 14).
Doctor: How have you been feeling lately? Is there anything in particular that's been bothering you?
Patient: I've been sleeping okay, but when wake up feel completely drained, if haven't feels tough; after reading for bit my mind just stops working, and I often drift off thinking about random things---I - don't t even know what I' I'm thinking about
slept
Doctor: Besides zoning out during study, are you able focus on other daily activities?
Patient: I get distracted with other things too Yesterday while running, my mind kept racing about homework and couldn' keep going.... It feels like can' concentrate anything for very long
Doctor: How long has this difficulty focusing lasted? Have you noticed any significant mood swings?
Patient: It been about three months, and my mood been pretty unstable. Sometimes suddenly feel very down, like can' muster the energy to anything my favorite games don' appeal to me.
Doctor: Did anything significant happen three months ago that caused you of stress? When you feel low, how long does it usually last?
intense. When felt down, sometimes couldn' shake it for a whole day-food food tasted bland.
Patient: That was finals time plus lot going on home, the
Doctor: Is your stress still high these days? Besides loss of appetite, any other physical
discomfort?
Patient: It' bit better but I still feel dizzy and unsteady when walking. Last night couldn' sleep until 2 AM.
Doctor: Is insomnia at night frequent? When did the dizziness start?
Patient: The dizziness began around last weekend, and tossing and turning at night has been common.
Doctor:
Have you experienced a similar situation since that last finals period in the past six
months?
Patient: Not as severe as then, but I still get anxious under study stress. Coffee doesn' t help foel foggy and my efficiency is low.
Doctor: Have you felt tense and uneasy every day recently?
Patient: Yes, my palms sweat in class, I lack appetite, and often wake up night and can' ' get
back to sleep.
Doctor: After waking up, what thoughts run through your mind?
Patient: think past exam failures, feel I didn' t study enough, and only calm down after checking my books.
Doctor: Does that checking calm you down?
Figure Full doctor patient dialogue in English (Page 1)
24

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Patient: little, but keep thinking about exams and only drift off around dawn, feeling listless in class the next day. yawning constantly.
Doctor: How long has this sleep disturbance been going on?
Patient: Looking at books helps a bit, but my mind keeps worrying about exams ... tossing and turning unable sleep--.ofte not drifting off until or AM and only just manage fall asleep,
I feel listless in class the next day, yawning constantly.
Doctor: How your appetite recently? Poor sleep must affect eating, right?
Patient: My appetite okay, but poor sleep has shifted my meal times; sometimes only have two bites at lunch around 2 or PM and I'm full.
Doctor: Half a year ago, did you have periods of several months feeling particularly tense and worried?
Patient: Indeed, about six months ago did feel especially tense and worried for some time, couldn't sleep, woke up feeling panicky, constantly worried about failing exams or something happening to my family, and at that time couldn't even bothered to play my favorite basketball.
Doctor: Apart from exams and worrying about family, is there anything else that's been particularly concerning you?
Patient: I was also always thinking about graduating and finding a job, staying up late browsing recruitment websites, afraid of not finding good job and disappointing my family, and even food tasted bland.
Doctor: Has that intense worry recurred intermittently from last year until now?
Patient: Yes, that feeling come gone since last year, especially when see classmates landing internships or jobs, panic, fearing falling behind, often unable to sleep at night while scrolling recruitment info, and I wake up groggy the next day.
Doctor: When that feeling of panic arises, can you control yourself and stop thinking about it?
Patient: Sometimes I try distract myself by running listening music, but after while
those thoughts return and 1 start worrying again.
Doctor: Do you feel utterly exhausted every day?
Patient: I feel especially tired every day, zone out in class, and stayed up late watching shows
last night and still haven' recovered..
Doctor: Are you experiencing any physical discomfort now?
Patient: It's just that I feel bit dizzy, my whole body lacks strength, my limbs foel weak, like
haven't slept enough, can't even muster the energy to walk.
Doctor: When you feel dizzy, do you feel particularly irritable inside?
Patient: When I'm dizzy, feel restless, wanting to stand up and walk, but I'm too tired to move,
keep adjusting my posture in the chair to comfortable.
Doctor: Lately, do you get angry over small things easily?
Figure 10: Full doctor paatert-ptienttttt dialogue in English (Page 2)
25

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Patient: Recently do get a bit irritable; last week, because my roommate threw garbage in the wrong place, 1 couldn' t help but loudly reprimand him. Normally I wouldn' react like that, maybe it's the stress.
Doctor: When working or studying, do you often zone out feel like your mind goes blank?
Patient: I zone out when studying; the other day was reading and suddenly spaced out for a few minutes before snapping back. hadn' done that before; maybe it' because of poor sleep recently.
Doctor: These days, how long does it take you to fall asleep once you lie in bed at night?
Patient: takes about half an hour fall asleep; sometimes I toss and turn thinking about things, I don' even touch my phone on the nightstand, just can' t fall asleep, and wake up the next day
with sore eyes.
Doctor: How has your mood been these past few days? Have you felt particularly depressed or unmotivated!
Patient: It' been okay, just sometimes wake up feeling especially tired even after a long sleep. After lunch get slcepy and want to nap but worry I won't sleep at night, so force myself not to, and after afternoon classes feel drained, and I don't want
just lie in bed. Doctor: When you lack appetite, do you have any other discomfort? Like stomach ache or dizziness?
Lately when eating feel some bloating, but not pain. m not dizzy, just often feel hazy, spaced-out sensation, especially in class staring out the window and realizing the lecturer has gone through several PPT slides.
Patient:
Doctor: How long has this hazy zoning out continued? Can you focus through an entire class?
Patient: During the last class I zoned out halfway, stared the outside for a long time
before snapping back and found that the teacher had moved on to the next chapter; my notebook pages were blank with notes.
Doctor: When did this zoning out start? Is every day?
Patient: It started about two weeks ago; not every day but very frequently. Sometimes when
chatting with friends suddenly blank out; my mind goes completely empty.
Doctor: How long has this blanking out during conversations with friends lasted? How long
before you snap back?
Patient: Since last week; when talking with friends suddenly stare blankly, it' s about a few scconds before snap back, and they think I' m daydrcaming.
Doctor: Has this blank mind sensation affected your eating sleeping?
Patient: Eating and sleeping are okay, but sometimes while eating my mind goes blank and my chopsticks just hang there until come to and continue; friends say I' m like frozen robot.
Doctor: Are you still interested in your usual hobbies? Do you find them less enjoyable than
before?
Patient: lost interest running; I used go the field daily but now only once twice week. It feels pointless; I don' teven feel like listening to music.
Figure 11: Full doctor-patient dialogue in English (Page 3)
26

---

Has this feeling lasted more than week? Besides running and music, are other activities also unappealing?
Doctor:
Patient: Reading feels boring; the other day bought book and read two pages before setting it aside. T don' t feel like watching shows during meals; just shovel few bites and that' stt.
Doctor: Have you recently felt particularly down, or have others remarked you seem sad?
Patient: Lately feel unmotivated do anything: don' 1 want play my favorite games, canceled friends' invitations to play ball, and just want to sit alone and stare.
Doctor: this mood been going on more than two weeks? Any impact on sleeping night?
Patient: It' s okay; I can sleep, but I wake still feeling tired, too drained for class or hanging out with friends.
Doctor: During this time, have you felt everything is meaningless?
Patient: I feel unmotivated; don' want to play my favorite games, and just mechanically cat meals; it feels meaningless.
Doctor: How long has this condition lasted?
Patient: About three weeks. I mechanically get up. attend class, then back to the dorm and scroll on my phone. I' m too lazy go the cafeteria, and I don' want to play basketball with my roommate.
Doctor: How" your appetite latcly? Any change in weight?
Patient: My appetite is okay, weight hasn" changed much. I lack appetite II often order takeout and eat only couple bites.
Doctor: Can you get enough sleep at night?
seven eight hours but sometimes wake once and toss and turn before fall back asleep, and I' m a bit sleepy in class.
Patient: I sleep reasonably
Doctor: Have you recently thought life is meaningless?
Patient: I do feel unmotivated; even don' want to play my favorite basketball. I lie down,
scroll my phone kill time, and make excuses to decline friends' invitations.
Doctor: During this period, is it easy for you to get distracted when doing things?
Patient: It' always hard concentrate; read pages before zoning out, and in lectures I
start thinking about other things-ni my efficiency is really low.
Doctor: Have you been feeling tired lately? Do you still have enough energy?
Patient: I do feel tired; it' hard to get up in the morning, and get winded running more casily, but still manage work out about half hour each day.
Doctor: Do you feel there is anything you' re proud of?
Patient: Not much be proud of just sticking half-hour run daily; even when don' feel
like moving, go, so that' s maybe small progress.
Doctor: Recently, have you felt especially irritable or unable to still?
Patient: I do feel restless; I often have to stand and walk around, especially in class, feeling uncomfortable and needing to change posture and then.
Figure 12: Full doctor patiinn dialogue in English (Page 4)
27

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Doctor: Lately, have you suddenly felt very excited or in a particularly good mood?
Patient: No, I haven' t felt excited or in particularly good spirits; most of the time it' flat.
Occasionally going out to eat with friends makes me happy. but it passes quickly.
Doctor: In the past month, have there been days where you frequently feel like snapping?
Patient: haven' t really snapped this past month; sometimes zone out in class and feel tired and want break.
Doctor: Have you ever felt that you were invincible?
Patient: I did feel that way before, especially in high school when the academic pressure intense believed that as long as worked hard could do well, stayed up late studying without feeling tired, and loved showing off my grades and abilitics to elassmates. Now it scems a bit laughable and unrealistic. Honestly, haven' felt that way in long time. Since university I ve become more aware of my limitations and shortcomings. Mainly because found I can' t do everything well-for example, often don' understand what physics teacher is saying class and do poorly on exams. Also, after joining some clubs, realized my organizational and coordination skills are really poor. Lately often feel pretty incompetent and useless...
Doctor: How do cope when you don' understand the physics class?
Patient: I grit my teeth and listen; if that doesn ' work check the texthook ask classmates. don' t want the teacher to see I don' understand.. Sometimes go to the library for reference materials, but it' not very effective and still often can' understand those formulas.
Doctor: How long have you been feeling incompetent and useless?
Patient: That feeling started about last year when academic pressure was intense. Seeing others keeping up with coursework made me feel useless, unable understand basic physics formulas, feeling frustrated every day.
Doctor: Do you still participate club activities now or have you given up?
Patient: I rarely attend club activitics now; after that physics cxam failure last year I quit the robotics club because felt embarrassed and didn' want face others. Occasionally play games in my dorm to pass time, but always feel unmotivated.
Doctor: Can playing games recently temporarily make you forget those frustrations?
Patient: When playing games still think about those things. space out and lose I get more irritated, then shut off and stare blankly all day.
Doctor: Last time you said you spaced out all day; how did you feel? Did you have any overly
negative extreme thoughts?
Patient: When spacing out my mind is blank and don' think of amything in particular; I just sit and gaze out the window
my phone sereen. Time passes quickly; when come to it' dark and sometimes forget to eat lunch.
Doctor: When spacing out, do you feel especially tired or suddenly energized?
Patient: When spacing out don' fcel tired energized just calmly sitting with slightly dry
eyes that I blink or rub. Occasionally I hear a dog barking downstairs and realize how much time has passed.
Figure 13: Full doctor-patient dialogue in English (Page 5)
28

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Are you usually active? Do you exercise regularly? Patient: usually jog on the field for half hour and go the gym for strength training on weekends. I feel my fitness is okay, though sometimes I feel tired after running and want to sleep early.
Doctor:
Doctor: Feeling tired after running and wanting to sleep early? Do you drink a lot of coffee during the day to stay alert?
Patient: don' drink much coffee during the day: occasionally buy latte before morning class to refresh, but don' dare drink too much for fear of insomnia affecting next-day studies; basically one cup per day.
Doctor: Has anyone in your family had emotional problems?
Patient: My father has Alzheimer' and my uncle suffers from severe depression. That does
worry me sometimes, fearing might inherit those issues.
Doctor: Have you tried using alcohol other means alleviate this worry recently?
Patient: haven' tried alcohol or other means to ease the worry. I just run daily and do strength
training: I feel my physical condition is okay and can keep negative thoughts in check.
Doctor: Besides exercise, do you have other relaxation methods? How is your sleep?
Patient: Lately listen soft music podcasts before bed, sometimes have cup of warm milk.
My sleep is okay; can generally get around seven hours.
Doctor: Diagnosis complete: Past major depressive episode and past generalized anxiety disorder.
Figure 14: Full doctor-patient dialogue in English (Page 6)
29


Reviews:
[{"review_id": "9EaIIb3Ggm", "reviewer": "Reviewer_rh5N", "summary": "This paper introduces a clinically grounded framework and dataset for diagnostic dialogues in the context of psychiatric comorbidity, aiming to bridge the gap between static medical records and interactive diagnostic conversations. The authors derive structured psychiatric dialogue data from clinical records and design a dialogue system that models comorbidity-aware diagnostic reasoning. The dataset includes multi-turn dialogues reflecting realistic clinician–patient interactions and overlapping psychiatric conditions, a setting that is significantly underrepresented in existing diagnostic dialogue research.", "questions": "- How do you envision safe and appropriate use of this dataset, given the risk of non-professional misuse of psychiatric diagnostic tools?\n- Were any psychiatrists, clinical psychologists, or licensed clinicians involved in reviewing the dataset or annotation guidelines? If so, please include details.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Clinically meaningful and socially impactful contribution. Psychiatric comorbidities are extremely common in real clinical practice, yet rarely addressed in diagnostic dialogue datasets. The paper fills an important gap by designing dialogues that reflect comorbidity patterns, symptom overlap, and ambiguity, which are critical challenges for mental health assessments. \n- Clear dataset design and annotation strategy. The paper provides a transparent methodology for constructing dialogues, annotating comorbid conditions, and representing psychiatric symptomatology in a conversation-friendly format. The schema and examples help convey the complexity handled by the dataset.\n- Well-written and structured. The paper is generally clear and does a good job situating itself relative to existing medical dialogue datasets, noting that most lack comorbidity or clinically grounded language patterns.", "weaknesses": "- Lack of rigorous evaluation. The paper does not present systematic evaluation of the dataset’s usefulness beyond illustrative examples. No comparisons or user studies (e.g., models trained with vs. without this dataset) are provided to show the dataset’s impact on model performance or clinical reasoning.\n- Limited novelty in methodology. The main novelty is the dataset’s domain focus. The data transformation pipeline is not sufficiently innovative or thoroughly justified for ICLR.\n- The dataset description could better highlight diversity: e.g., demographic representation, cultural/linguistic bias, and how psychiatric conditions with culturally variant presentations were handled.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "3KeNzWvuhx", "reviewer": "Reviewer_SXir", "summary": "This paper presents PsyCoTalk, a clinically grounded dataset for psychiatric comorbidity diagnosis. It introduces a two-stage pipeline:\n(1) PsyCoProfile, which constructs 502 synthetic electronic medical records (EMRs) for patients with multiple co-occurring disorders (MDD, AD, BD, ADHD), generated from social media posts; and\n(2) PsyCoTalk, which uses a multi-agent framework guided by SCID-5-RV (a DSM-5 diagnostic interview standard) to produce 3,000 multi-turn diagnostic dialogues.\nThe dataset is validated by psychiatrists and claimed to be the first resource that supports comorbid diagnostic reasoning in simulated doctor–patient interactions.", "questions": "Please check the weakness part", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "(1)Novel focus on psychiatric comorbidity: Unlike prior mental disorder datasets that focus on single disorders, this work explicitly targets psychiatric comorbidity, which is a clinically important setting.\n(2)Multi-agent framework: Integrating doctor, patient, and tool agents under a hierarchical diagnostic state machine (HDSM) is interpretable.\n(3)Psychiatrist validation: Involvement of licensed psychiatrists adds credibility to the dataset’s linguistic and diagnostic realism.", "weaknesses": "(1)Data effectiveness: Since all EMRs and dialogues are synthetic, derived from social media posts and LLM-based generation, can the dataset truly reflect authentic doctor–patient interactions? Do the linguistic patterns or emotional tone in these generated dialogues capture the depth and subtlety of real psychiatric interviews? Without any real clinical data for grounding or comparison, how credible is the claim of “clinical realism”?\n(2)Simplified symptom representation: By reducing the SCID-5’s original four-point symptom scale to binary “present/absent” labels, does the dataset lose essential diagnostic nuance and symptom severity? How might this simplification affect the interpretability and downstream clinical reliability of models trained on such data?\n(3)Lack of Inter-Disorder Relationship Modeling: If “comorbidity” here merely refers to co-labeled samples without modeling relationships or dependencies between disorders, can it truly represent comorbid diagnostic reasoning? Within the dialogues, do co-occurring disorders interact dynamically, or are they treated as independent diagnostic categories? To what extent does the dialogue structure capture overlapping or interacting symptoms?\n(4)Evaluation limitations: Given that only 50 dialogues were rated by five psychiatrists, is this sample sufficient to demonstrate the dataset’s overall reliability, linguistic realism, and diagnostic accuracy? How representative are those 50 evaluated samples compared to the entire corpus of 3,000 dialogues, and how consistent are the expert ratings?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "C6YMKnzq47", "reviewer": "Reviewer_ZBWt", "summary": "This paper presents PsyCoTalk, a large-scale, clinically grounded dataset of 3,000 multi-turn psychiatric diagnostic dialogues generated from 502 synthetic electronic medical records.\nThe authors propose a multi-agent framework simulating doctor–patient interactions, guided by a Hierarchical Diagnostic State Machine  and a Diagnostic Context Tree based on DSM-5 structured interview standards.\n\nThe dataset focuses on psychiatric comorbidity, i.e., the co-occurrence of multiple mental disorders. Expert psychiatrists validated the data for realism and clinical validity, finding that PsyCoTalk conversations are close to real diagnostic interviews. The authors argue that this dataset will support the development of AI systems for multi-disease mental health reasoning and diagnostic support.", "questions": "N/A", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "The pipeline integrates SCID-5 logic, diagnostic state transitions, and contextual reasoning, providing a strong medical foundation rarely seen in synthetic dialogue work.\n\nCombines multi-agent dialogue simulation with structured EMR synthesis — a novel hybrid between symbolic reasoning and LLM-based text generation.\n\nFirst dataset to explicitly address psychiatric comorbidity through structured, clinically grounded dialogues.", "weaknesses": "The synthetic medical records and the generated dialogues come from the same design logic. The “doctor” agent is judged against data that the system itself produced. This makes it hard to know whether the model is learning genuine clinical reasoning or just reproducing patterns it already encoded.\n\nThe diagnostic flow treats symptoms as mostly binary (“present” or “absent”), while real clinicians deal with uncertainty, partial symptoms, and differential diagnoses. The result may teach models to classify too confidently.\n\nThe dataset only covers a small set of disorder combinations (mainly depression, anxiety, bipolar, ADHD). Other common co-occurring conditions like PTSD or substance use are missing, so “comorbidity” here is still narrow.\n\nThe evaluation mainly compares PsyCoTalk to versions of itself (with or without the diagnostic controller). There’s no test showing whether this data helps models perform better on external or real-world benchmarks.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "beo6rpiQ2K", "reviewer": "Reviewer_tWSs", "summary": "This paper presents a novel two-stage pipeline to address the challenge of diagnosing psychiatric comorbidity. First, the paper creates PsyCoProfile, a set of 502 synthetic, clinically-grounded Electronic Medical Records (EMRs) for patients with common comorbid disorders, derived from social media data. Second, the paper develops a multi-agent framework combining a hierarchical diagnostic state machine and the diagnosis context tree for diagnosis. Lastly, the paper introduces PsyCoTalk, the first large-scale dataset of 3,000 multi-turn diagnostic dialogues for psychiatric comorbidity, which has been validated by licensed psychiatrists for its clinical realism and diagnostic validity. This work provides a crucial resource for training and evaluating models capable of multi-disorder psychiatric screening.", "questions": "1.Could the authors clarify the release plan for the assets created in this work? Specifically, will the full dataset of 502 synthetic EMRs (PsyCoProfile) be released alongside the 3,000 multi-turn diagnostic dialogues (PsyCoTalk)? \n\n2.The paper demonstrates the quality of the dataset but does not establish a performance benchmark for models trained on it. To demonstrate the dataset's utility for downstream tasks, could the authors provide a performance report for a baseline model trained on PsyCoTalk for multi-disorder diagnosis? This would provide a crucial point of comparison for future works.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.The paper proposes PsyCoTalk, the first large-scale dialogue dataset specifically targeting psychiaatric comorbidity.\n\n2.The paper's claims are substantiated by a rigorous and multi-faceted evaluation protocol. The results, particularly the high realism scores in an AB test against real-world dialogues, provide strong evidence for the dataset's clinical plausibility and validity.\n\n3.The paper is well-presented, facilitating a strong understanding of the work's mechanics and contributions.", "weaknesses": "1.The model simplifies clinical symptoms to a basic \"yes/no\" answer, losing the nuance of real-world diagnosis where symptoms can be mild or subthreshold.\n\n2.The framework relies on a manually-crafted state machine. While it can guarantee clinical accuracy, this approach is rigid and labor-intensive. Scaling the system to include more diseases would require significant expert effort to design and implement new state-machine modules, making the entire pipeline difficult to expand.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)