Title: From Indicators to Insights: Diversity-Optimized for Medical Series-Text Decoding via LLMs
Venue: NeurIPS 2025
Accepted: yes
Keywords: biomedical signal analysis

medical time series

knowledge-guided learning
Abstract: Medical time-series analysis differs fundamentally from general ones by requiring specialized domain knowledge to interpret complex signals and clinical context.
Large language models (LLMs) hold great promise for augmenting medical time-series analysis by complementing raw series with rich contextual knowledge drawn from biomedical literature and clinical guidelines.
However, realizing this potential depends on precise and meaningful prompts that guide the LLM to key information.
Yet, determining what constitutes effective prompt content remains non-trivial—especially in medical settings where signal interpretation often hinges on subtle, expert-defined decision-making indicators. 
To this end, we propose InDiGO, a knowledge-aware evolutionary learning framework that integrates clinical signals and decision-making indicators through iterative optimization.
Across four medical benchmarks, InDiGO consistently outperforms prior methods. 
The code is available at: https://github.com/jinxyBJTU/InDiGO.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper emphasizes the importance of prompt design in extracting contextual information from LLMs. Motivated by this, the authors propose a knowledge-informed evolutionary learning framework, InDiGO, which incorporates expert-defined clinical indicators into the prompt optimization process.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n\n1. The motivation is well-grounded, addressing a meaningful gap in combining expert knowledge with LLM-based time-series interpretation.\n2. The idea of using indicator-guided prompt construction is novel and intuitive, leading to prompts that better align with clinical reasoning.\n\n\n## Weaknesses\n\n1. The method requires external tools to extract clinical indicators (e.g., RR intervals, QRS durations) for each input sequence, which are then integrated into prompts. This introduces additional computational overhead and may hinder real-time inference. However, the authors do not report the cost of computing these indicators.\n2. The proposed prompt optimization strategy is based on randomly masking parts of the initial prompt. It is unclear why masking informative parts of the prompt would lead to better performance, as complete prompts presumably contain richer semantics. Compared to this, prior work such as [1] employs LLMs to iteratively generate semantically different prompts, which may offer more principled and interpretable variations.\n3. Several key experimental details remain insufficiently reported, making it difficult to fully assess the method’s efficiency and fairness. See the Questions section for specific concerns.\n\n[1] Guo et al. (2024). Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers. ICLR.\n\nQUESTIONS:\n1. The authors should provide further explanation for why randomly masking tokens in the prompt leads to better model performance. What exactly is meant by “inaccurate prompts” in this context, and why are masked variants considered more “accurate”? A clearer justification, both empirically and conceptually, is needed.\n2. Could the authors clarify the efficiency evaluation setup in Section 5.5? What are the batch sizes and memory usage for each model? Were the computational costs of extracting indicators (e.g., RR intervals) included in the runtime? In real-world deployment, these indicators would need to be computed online. Please provide details on the tools used and their computational overhead.\n3. It would be helpful to report statistics on the label distribution for each dataset. In medical classification tasks, label imbalance is common and could significantly affect performance metrics. In such cases, metrics like AUC might be more appropriate than accuracy.\n\nLIMITATIONS:\nThe authors adequately addressed the limitations of their work.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-18"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this paper, the authors propose a new method for integrating medical time series data with large language models. The proposed method, called InDiGO, is derived using masked importance sampling. The method seems to have strong performance in numerical experiments.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: The work is well-motivated by important real world scientific applications of AI. The visual aids (in particular Figures 2 and 3) are very well-made and \n\nWeakness: The weaknesses of the paper lie in its presentation of the proposed InDiGO method. First of all, the assumption that the class-label probabilities follow a normal distribution in equation (1) does not make any sense. Since the rest of the method follows from this assumption, I am concerned about the theoretical validity of the proposed algorithm. Additionally, the authors spend a lot of space in Section 4 detailed how the importance sampling method is derived; however, they do not provide sufficient context for how their method compares to previous works in the literature. As such, I have no sense at all of the methodological innovation of InDiGO over existing methods.\n\nAdditionally, the experimentation section is unclear and poorly written. It is not apparent at all from the main body of the text what the specific tasks on which the proposed method and its competitors are evaluated. While additional details are given in the appendix, as the paper is currently written it is difficult to parse the numerical results and evaluate them critically.\n\nQUESTIONS:\n1. How does your InDiGO method compare methodologically to existing methods for integrating time series data and LLM's in the literature?\n\n2. To what extent does the performance of InDiGO depend on the normality assumption in equation (1)? Can this be modified or relaxed?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 1, "IMPACT": 1, "ORIGINALITY": 2, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nCompared to general-domain time-series analysis, the medical domain requires more domain-specific knowledge – for example, understanding what QRS complexes and R-R intervals are, and how they influence diagnosis or prognosis. Therefore, researchers explore knowledge integration into time-series analysis in different forms, such as designing task-specific architectures and explicitly feeding necessary knowledge within an LLM’s context. Nonetheless, current explicit medical knowledge integration approaches remain limited in performance, mainly due to a lack of specificity in the prompts and only conceptual integration of knowledge, ignoring the interdependent nature of medical phenomena. To this end, the authors propose InDiGO, which synergises time-series and domain knowledge prior to pre-trained LLM integration. InDiGO incorporates three important mechanisms that boost performance noticeably: constructing prompt prototypes guided by task-relevant indicators; using masked Monte Carlo sampling to reduce existing bias in manually designed prompts; and finally combining match alignment and diversity optimisation to boost performance and promote the diversity of selected prompts. The authors demonstrate leading performance with their method across multiple tasks and datasets, perform extensive ablation studies to identify how each component contributes to the final performance, and include visualisations illustrating the qualitative effects of their work.\n\nSTRENGTHS AND WEAKNESSES:\n### **Strengths**\n\n1. The experimental setup is strong. The authors used various prevalent datasets for different tasks. The ablations in the paper were necessary and on point. Including case studies such as low-resource generalisation provides a broader picture of InDiGO's impact.\n2. Although the work combines the recent advances in time-series analysis with LLMs and medical analysis by injecting related 'indicators' dynamically, and using latest improvements in time-series analysis with LLMs, the product is original and can be leveraged in other domains that require external knowledge such as financial analysis.\n1. InDiGO performs the best compared to baselines across the board. Although the most similar framework to InDiGO is KEDGN in terms of domain and recency, InDiGO significantly outperforms it while introducing training and inference efficiency.\n3. The mathematical formulation was clear and understandable, and equations follow to the best of my capabilities\n5. The flow of the paper is nice, clear examples \n\n### **Weaknesses**\n1. Although using masked Monte Carlo sampling can reduce the existing bias in manually designed prompts, the results are still extremely dependent on the expert-curated prompt prototypes. Nonetheless, the authors do not present an analysis regarding the relationship between these prototypes and InDiGO's performance.\n2. While the masked Monte Carlo sampling reduces bias (shown contributions mathematically and empirically), it is not clear to me why the authors have not utilised multiple prototype candidates for a single prototype, such that the bias would be even smaller.\n3. Figure 2 is bloated and it is hard to follow the flow and the steps depending on the figure alone. Although the text helps clairfying it to a certain degree, the figure can be improved for easier and quicker understanding.\n\nQUESTIONS:\nN/A\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\n- The work tackles medical time‑series classification by injecting decision‑making indicators (e.g., sleep spindles, RR intervals) into prompt design, a type of knowledge current LLM approaches largely ignore.  \n- The proposed method creates an indicator‑aware prompt prototype, then uses mask‑based Monte Carlo importance sampling plus additional training objectives to pick robust series‑text combinations.  \n- Experiments on four public datasets show consistent gains over existing baselines.  \n- Ablations confirm each module – sampling, series‑text interaction, alignment, diversity – contributes to achieving best performacne\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n- *Quality* - Paper has wide empirical coverage: multiple datasets, extensive set of baselines, plus methodological ablations.  \n- *Significance* - Shows that lightweight indicator prompts can outperform heavier multimodal models, potentially easing clinical adoption.  \n- *Originality* - In my opinion, the paper presents a novel combination of indicator‑guided prompt construction, smartly combining LLM prompts with tokenized time series.\n- *Clarity* - The mathematical treatment of sampling and losses is rigorous and detailed. \n\n\n## Weaknesses\n- *Quality* - Indicators must be **hand‑specified per task**, limiting out‑of‑the‑box generalization; authors themselves note dependence on structured indicator extraction.  \n- *Complexity* - Pipeline stitches multiple pretrained blocks and requires repeated prompt sampling; compute overhead vs. simpler fusion models is not fully quantified.  \n- *Presentation* - Presentation can be improved. There are some typos such as “ralated” and “RR_intervael”. Additionally, Figure 2 is densely packed and hard to correctly parse.  \n- *Experiments* - No single‑prompt baseline (m=1) nor variant with simpler cross‑attention variants.\n\nQUESTIONS:\n1. What concrete criteria did you consider for choosing indicators per task? Were any indicators left out during part of the development process?\n2. Is co‑attention strictly necessary, or could a single cross‑attention layer achieve similar gains? In many Perceiver-like setups, it is often enough with only one type of cross-attention between feature vectors.\n3. Can you please report results for m=1 to determine the benefit of prompt diversity.  \n4. Does the GPT‑2 encoder half still use causal masks? If so, why is autoregressive masking desirable for encoding?\n\nLIMITATIONS:\nThe paper does admit reliance on manual indicator extraction but does not measure robustness to noisy or missing indicators. Please consider adding experiments with perturbed indicators, and maybe discussing ethical risks of wrong prompts.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-19"}