GenAI4ELab/papercli-papers huggingface dataset:
Explainability for NLP in Pharmacovigilance: A Study on Adverse Event Report Triage in Swedish
Authors: Luise Dürlich; Erik Bergman; Maria Larsson; Hercules Dalianis; Seamus Doyle; Gabriel Westman; Joakim Nivre
Venue: NAACL 2025 (Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health))

Abstract: In fields like healthcare and pharmacovigilance, explainability has been raised as one way of approaching regulatory compliance with machine learning and automation.This paper explores two feature attribution methods to explain predictions of four different classifiers trained to assess the seriousness of adverse event reports. On a global level, differences between models and how well important features for serious predictions align with regulatory criteria for what constitutes serious adverse reactions are analysed. In addition, explanations of reports with incorrect predictions are manually explored to find systematic features explaining the misclassification.We find that while all models seemingly learn the importance of relevant concepts for adverse event report triage, the priority of these concepts varies from model to model and between explanation methods, and the analysis of misclassified reports indicates that reporting style may affect prediction outcomes.
When Multilingual Models Compete with Monolingual Domain-Specific Models in Clinical Question Answering
Authors: Vojtech Lanz; Pavel Pecina
Venue: NAACL 2025 (Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health))

Abstract: This paper explores the performance of multilingual models in the general domain on the clinical Question Answering (QA) task to observe their potential medical support for languages that do not benefit from the existence of clinically trained models. In order to improve the model’s performance, we exploit multilingual data augmentation by translating an English clinical QA dataset into six other languages. We propose a translation pipeline including projection of the evidences (answers) into the target languages and thoroughly evaluate several multilingual models fine-tuned on the augmented data, both in mono- and multilingual settings. We find that the translation itself and the subsequent QA experiments present a differently challenging problem for each of the languages. Finally, we compare the performance of multilingual models with pretrained medical domain-specific English models on the original clinical English test set. Contrary to expectations, we find that monolingual domain-specific pretraining is not always superior to general-domain multilingual pretraining. The source code is available at https://github.com/lanzv/Multilingual-emrQA
Mining Social Media for Barriers to Opioid Recovery with LLMs
Authors: Vinu Ekanayake; Md Sultan Al Nahian; Ramakanth Kavuluru
Venue: NAACL 2025 (Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health))

Abstract: Opioid abuse and addiction remain a major public health challenge in the US. At a broad level, barriers to recovery often take the form of individual, social, and structural issues. However, it is crucial to know the specific barriers patients face to help design better treatment interventions and healthcare policies. Researchers typically discover barriers through focus groups and surveys. While scientists can exercise better control over these strategies, such methods are both expensive and time consuming, needing repeated studies across time as new barriers emerge. We believe, this traditional approach can be complemented by automatically mining social media to determine high-level trends in both well-known and emerging barriers. In this paper, we report on such an effort by mining messages from the r/OpiatesRecovery subreddit to extract, classify, and examine barriers to opioid recovery, with special attention to the COVID-19 pandemic’s impact. Our methods involve multi-stage prompting to arrive at barriers from each post and map them to existing barriers or identify new ones. The new barriers are refined into coherent categories using embedding-based similarity measures and hierarchical clustering. Temporal analysis shows that some stigma-related barriers declined (relative to pre-pandemic), whereas systemic obstacles—such as treatment discontinuity and exclusionary practices—rose significantly during the pandemic. Our method is general enough to be applied to barrier extraction for other substance abuse scenarios (e.g., alcohol or stimulants)
Multimodal Transformers for Clinical Time Series Forecasting and Early Sepsis Prediction
Authors: Jinghua Xu; Michael Staniek
Venue: NAACL 2025 (Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health))

Abstract: Sepsis is a leading cause of death in Intensive Care Units (ICU). Early detection of sepsis is crucial to patient survival. Existing works in the clinical domain focus mainly on directly predicting a ground truth label that is the outcome of a medical syndrome or condition such as sepsis. In this work, we primarily focus on clinical time series forecasting as a means to solve downstream predictive tasks intermediately. We base our work on a strong monomodal baseline and propose multimodal transformers using set functions via fusing both physiological features and texts in electronic health record (EHR) data. Furthermore, we propose hierarchical transformers to effectively represent clinical document time series via attention mechanism and continuous time encoding. Our multimodal models significantly outperform baseline on MIMIC-III data by notable gaps. Our ablation analysis show that our atomic approaches to multimodal fusion and hierarchical transformers for document series embedding are effective in forecasting. We further fine-tune the forecasting models with labelled data and found some of the multimodal models consistently outperforming baseline on downstream sepsis prediction task.
Comparing representations of long clinical texts for the task of patient-note identification
Authors: Safa Alsaidi; Marc Vincent; Olivia Boyer; Nicolas Garcelon; Miguel Couceiro; Adrien Coulet
Venue: NAACL 2025 (Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health))

Abstract: In this paper, we address the challenge of patient-note identification, which involves accurately matching an anonymized clinical note to its corresponding patient, represented by a set of related notes. This task has broad applications, including duplicate records detection and patient similarity analysis, which require robust patient-level representations. We explore various embedding methods, including Hierarchical Attention Networks (HAN), three-level Hierarchical Transformer Networks (HTN), LongFormer, and advanced BERT-based models, focusing on their ability to process medium-to-long clinical texts effectively. Additionally, we evaluate different pooling strategies (mean, max, and mean_max) for aggregating word-level embeddings into patient-level representations and we examine the impact of sliding windows on model performance. Our results indicate that BERT-based embeddings outperform traditional and hierarchical models, particularly in processing lengthy clinical notes and capturing nuanced patient representations. Among the pooling strategies, mean_max pooling consistently yields the best results, highlighting its ability to capture critical features from clinical notes. Furthermore, the reproduction of our results on both MIMIC dataset and Necker hospital data warehouse illustrates the generalizability of these approaches to real-world applications, emphasizing the importance of both embedding methods and aggregation strategies in optimizing patient-note identification and enhancing patient-level modeling.
MeDiSumQA: Patient-Oriented Question-Answer Generation from Discharge Letters
Authors: Amin Dada; Osman Koras; Marie Bauer; Amanda Butler; Kaleb Smith; Jens Kleesiek; Julian Friedrich
Venue: NAACL 2025 (Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health))

Abstract: While increasing patients’ access to medical documents improves medical care, this benefit is limited by varying health literacy levels and complex medical terminology. Large language models (LLMs) offer solutions by simplifying medical information. However, evaluating LLMs for safe and patient-friendly text generation is difficult due to the lack of standardized evaluation resources. To fill this gap, we developed MeDiSumQA. MeDiSumQA is a dataset created from MIMIC-IV discharge summaries through an automated pipeline combining LLM-based question-answer generation with manual quality checks. We use this dataset to evaluate various LLMs on patient-oriented question-answering. Our findings reveal that general-purpose LLMs frequently surpass biomedical-adapted models, while automated metrics correlate with human judgment. By releasing MeDiSumQA on PhysioNet, we aim to advance the development of LLMs to enhance patient understanding and ultimately improve care outcomes.
Using LLMs to improve RL policies in personalized health adaptive interventions
Authors: Karine Karine; Benjamin Marlin
Venue: NAACL 2025 (Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health))

Abstract: Reinforcement learning (RL) is increasingly used in the healthcare domain, particularly for the development of personalized adaptive health interventions. However, RL methods are often applied to this domain using small state spaces to mitigate data scarcity. In this paper, we aim to use Large Language Models (LLMs) to incorporate text-based user preferences and constraints, to update the RL policy. The LLM acts as a filter in the action selection. To evaluate our method, we develop a novel simulation environment that generates text-based user preferences and incorporates corresponding constraints that impact behavioral dynamics. We show that our method can take into account the text-based user preferences, while improving the RL policy, thus improving personalization in adaptive intervention.
LLM Based Efficient CSR Summarization using Structured Fact Extraction and Feedback
Authors: Kunwar Zaid; Amit Sangroya; Lovekesh Vig
Venue: NAACL 2025 (Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health))

Abstract: Summarizing clinical trial data poses a significant challenge due to the structured, voluminous, and domain-specific nature of clinical tables. While large language models (LLMs) such as ChatGPT, Llama, and DeepSeek demonstrate potential in table-to-text generation, they struggle with raw clinical tables that exceed context length, leading to incomplete, inconsistent, or imprecise summaries. These challenges stem from the structured nature of clinical tables, complex study designs, and the necessity for precise medical terminology. To address these limitations, we propose an end-to-end pipeline that enhances the summarization process by integrating fact selection, ensuring that only the most relevant data points are extracted for summary generation. Our approach also incorporates a feedback-driven refinement mechanism, allowing for iterative improvements based on domain-specific requirements and external expert input. By systematically filtering critical information and refining outputs, our method enhances the accuracy, completeness, and clinical reliability of generated summaries while reducing irrelevant or misleading content. This pipeline significantly improves the usability of LLM-generated summaries for medical professionals, regulators, and researchers, facilitating more efficient interpretation of clinical trial results. Our findings suggest that targeted preprocessing and iterative refinement strategies within the proposed piepline can mitigate LLM limitations, offering a scalable solution for summarizing complex clinical trial tables.
On Large Foundation Models and Alzheimer’s Disease Detection
Authors: Chuyuan Li; Giuseppe Carenini; Thalia Field
Venue: NAACL 2025 (Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health))

Abstract: Large Foundation Models have displayed incredible capabilities in a wide range of domains and tasks. However, it is unclear whether these models match specialist capabilities without special training or fine-tuning. In this paper, we investigate the innate ability of foundation models as neurodegenerative disease specialists. Precisely, we use a language model, Llama-3.1, and a visual language model, Llama3-LLaVA-NeXT, to detect language specificity between Alzheimer’s Disease patients and healthy controls through a well-known Picture Description task. Results show that Llama is comparable to supervised classifiers, while LLaVA, despite its additional “vision”, lags behind.
Benchmarking IsiXhosa Automatic Speech Recognition and Machine Translation for Digital Health Provision
Authors: Abby Blocker; Francois Meyer; Ahmed Biyabani; Joyce Mwangama; Mohammed Ishaaq Datay; Bessie Malila
Venue: NAACL 2025 (Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health))

Abstract: As digital health becomes more ubiquitous, people from different geographic regions are connected and there is thus a need for accurate language translation services. South Africa presents opportunity and need for digital health innovation, but implementing indigenous translation systems for digital health is difficult due to a lack of language resources. Understanding the accuracy of current models for use in medical translation of indigenous languages is crucial for designers looking to build quality digital health solutions. This paper presents a new dataset with audio and text of primary health consultations for automatic speech recognition and machine translation in South African English and the indigenous South African language of isiXhosa. We then evaluate the performance of well-established pretrained models on this dataset. We found that isiXhosa had limited support in speech recognition models and showed high, variable character error rates for transcription (26-70%). For translation tasks, Google Cloud Translate and ChatGPT outperformed the other evaluated models, indicating large language models can have similar performance to dedicated machine translation models for low-resource language translation.
Preliminary Evaluation of an Open-Source LLM for Lay Translation of German Clinical Documents
Authors: Tabea Pakull; Amin Dada; Hendrik Damm; Anke Fleischhauer; Sven Benson; Noëlle Bender; Nicola Prasuhn; Katharina Kaminski; Christoph Friedrich; Peter Horn; Jens Kleesiek; Dirk Schadendorf; Ina Pretzell
Venue: NAACL 2025 (Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health))

Abstract: Clinical documents are essential to patient care, but their complexity often makes them inaccessible to patients. Large Language Models (LLMs) are a promising solution to support the creation of lay translations of these documents, addressing the infeasibility of manually creating these translations in busy clinical settings. However, the integration of LLMs into medical practice in Germany is challenging due to data scarcity and privacy regulations. This work evaluates an open-source LLM for lay translation in this data-scarce environment using datasets of German synthetic clinical documents and real tumor board protocols. The evaluation framework used combines readability, semantic, and lexical measures with the G-Eval framework. Preliminary results show that zero-shot prompts significantly improve readability (e.g., FREde: 21.4 → 39.3) and few-shot prompts improve semantic and lexical fidelity. However, the results also reveal G-Eval’s limitations in distinguishing between intentional omissions and factual inaccuracies. These findings underscore the need for manual review in clinical applications to ensure both accessibility and accuracy in lay translations. Furthermore, the effectiveness of prompting highlights the need for future work to develop applications that use predefined prompts in the background to reduce clinician workload.
Leveraging External Knowledge Bases: Analyzing Presentation Methods and Their Impact on Model Performance
Authors: Hui-Syuan Yeh; Thomas Lavergne; Pierre Zweigenbaum
Venue: NAACL 2025 (Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health))

Abstract: Integrating external knowledge into large language models has demonstrated potential for performance improvement across a wide range of tasks. This approach is particularly appealing in domain-specific applications, such as in the biomedical field. However, the strategies for effectively presenting external knowledge to these models remain underexplored. This study investigates the impact of different knowledge presentation methods and their influence on model performance. Our results show that inserting knowledge between demonstrations helps the models perform better, and improve smaller LLMs (7B) to perform on par with larger LLMs (175B). Our further investigation indicates that the performance improvement, however, comes more from the effect of additional tokens and positioning than from the relevance of the knowledge.
Explainable ICD Coding via Entity Linking
Authors: Leonor Barreiros; Isabel Coutinho; Gonçalo Correia; Bruno Martins
Venue: NAACL 2025 (Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health))

Abstract: Clinical coding is a critical task in healthcare, although traditional methods for automating clinical coding may not provide sufficient explicit evidence for coders in production environments. This evidence is crucial, as medical coders have to make sure there exists at least one explicit passage in the input health record that justifies the attribution of a code. We therefore propose to reframe the task as an entity linking problem, in which each document is annotated with its set of codes and respective textual evidence, enabling better human-machine collaboration. By leveraging parameter-efficient fine-tuning of Large Language Models (LLMs), together with constrained decoding, we introduce three approaches to solve this problem that prove effective at disambiguating clinical mentions and that perform well in few-shot scenarios.
Will Gen Z users look for evidence to verify QA System-generated answers?
Authors: Soumya Gayen; Dina Demner-Fushman; Deepak Gupta
Venue: NAACL 2025 (Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health))

Abstract: The remarkable results shown by medicalquestion-answering systems lead to theiradoption in real-life applications. The systems,however, may misinform the users, even whendrawing on scientific evidence to ground theresults. The quality of the answers maybe verified by the users if they analyze theevidence provided by the systems. Userinterfaces play an important role in engagingthe users. While studies of the user interfacesfor biomedical literature search and clinicaldecision support are abundant, little is knownabout users’ interactions with medical questionanswering systems and the impact of thesesystems on health-related decisions. In a studyof several different user interface layouts, wefound that only a small number of participantsfollowed the links to verify automaticallygenerated answers, independently of theinterface design. The users who followed thelinks made better health-related decisions.
Am I eligible? Natural Language Inference for Clinical Trial Patient Recruitment: the Patient’s Point of View
Authors: Mathilde Aguiar; Pierre Zweigenbaum; Nona Naderi
Venue: NAACL 2025 (Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health))

Abstract: Recruiting patients to participate in clinical trials can be challenging and time-consuming. Usually, participation in a clinical trial is initiated by a healthcare professional and proposed to the patient. Promoting clinical trials directly to patients via online recruitment might help to reach them more efficiently. In this study, we address the case where a patient is initiating their own recruitment process and wants to determine whether they are eligible for a given clinical trial, using their own language to describe their medical profile. To study whether this creates difficulties in the patient-trial matching process, we design a new dataset and task, Natural Language Inference for Patient Recruitment (NLI4PR), in which patient-language profiles must be matched to clinical trials. We create it by adapting the TREC 2022 Clinical Trial Track dataset, which provides patients’ medical profiles, and rephrasing them manually using patient language. We also use the associated clinical trial reports where the patients are either eligible or excluded. We prompt several open-source Large Language Models on our task and achieve from 56.5 to 71.8 of F1 score using patient language, against 64.7 to 73.1 for the same task using medical language. When using patient language, we observe only a small loss in performance for the best model, suggesting that having the patient as a starting point could be adopted to help recruit patients for clinical trials. The corpus and code bases are all freely available on our GitHub and HuggingFace repositories.
Towards Understanding LLM-Generated Biomedical Lay Summaries
Authors: Rohan Charudatt Salvi; Swapnil Panigrahi; Dhruv Jain; Shweta Yadav; Md. Shad Akhtar
Venue: NAACL 2025 (Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health))

Abstract: In this paper, we investigate using large language models to generate accessible lay summaries of medical abstracts, targeting non-expert audiences. We assess the ability of models like GPT-4 and LLaMA 3-8B-Instruct to simplify complex medical information, focusing on layness, comprehensiveness, and factual accuracy. Utilizing both automated and human evaluations, we discover that automatic metrics do not always align with human judgments. Our analysis highlights the potential benefits of developing clear guidelines for consistent evaluations conducted by non-expert reviewers. It also points to areas for improvement in the evaluation process and the creation of lay summaries for future research.
Towards Knowledge-Guided Biomedical Lay Summarization using Large Language Models
Authors: Shufan Ming; Yue Guo; Halil Kilicoglu
Venue: NAACL 2025 (Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health))

Abstract: The massive size, continual growth, and technical jargon in biomedical publications make it difficult for laypeople to stay informed about the latest scientific advances, motivating research on lay summarization of biomedical literature. Large language models (LLMs) are increasingly used for this task. Unlike typical automatic summarization, lay summarization requires incorporating background knowledge not found in a paper and explanations of technical jargon. This study explores the use of MeSH terms (Medical Subject Headings), which represent an article’s main topics, to enhance background information generation in biomedical lay summarization. Furthermore, we introduced a multi-turn dialogue approach that more effectively leverages MeSH terms in the instruction-tuning of LLMs to enhance the quality of lay summaries. The best model improved the state-of-the-art on the eLife test set in terms of the ROUGE-1 score by nearly 2%, with competitive scores in other metrics. These results indicate that MeSH terms can guide LLMs to generate more relevant background information for laypeople. Additionally, evaluation on a held-out dataset, one that was not used during model pre-training, shows that this capability generalizes well to unseen data, further demonstrating the effectiveness of our approach.