United States

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.

NAACL 2025

Multimodal Transformers for Clinical Time Series Forecasting and Early Sepsis Prediction

clinical time series forecasting

clinical notes

multimodal learning

clinical nlp

transformers

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

workshop paper

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- Colin Cherry, Google, NAACL 2025 General Chair* 

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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

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.

Explainable ICD Coding via Entity Linking

Chronic Kidney Disease (CKD) is a global health challenge, affecting 5–10% of the population, with a significant burden on healthcare systems. Early prediction of CKD progression from stage III to stage V is crucial to enable timely interventions. Traditional predictive methods rely on biochemical markers and demographic factors, but are often limited by issues such as missing data and reliance on structured inputs. This study explores the potential of several encoder-based language models, to predict CKD progression using a cohort from the Clinical Practice Research Datalink (CPRD) GOLD database. We applied both Full Fine-Tuning (FFT) and Parameter-Efficient Fine-Tuning (PEFT) with LoRA to pre-trained models, comparing them against traditional machine learning algorithms such as Random Forest and XGBoost. Our results show that fine-tuned models, particularly dmis-lab/biobert-v1.1-FFT, outperform traditional models in predicting CKD progression, with an AUC of 0.7787, precision of 0.7261, and accuracy of 0.7045. Although LoRA-based models are more computationally efficient, they consistenly exhibit lower performance. These findings suggest that fine-tuned encoder models hold significant potential for improving CKD progression prediction. However, there is still room for further enhancement in their accuracy and applicability in clinical settings.

Predicting Chronic Kidney Disease Progression from Stage III to Stage V using Language Models

Chronic pain affects millions, yet traditional assessments often fail to capture patients' lived experiences comprehensively. In this study, we used a Motivational Interviewing framework to conduct semi-structured interviews with eleven adults experiencing chronic pain and then applied Natural Language Processing (NLP) to their narratives. We developed an annotation schema that integrates the International Classification of Functioning, Disability, and Health (ICF) with Aspect-Based Sentiment Analysis (ABSA) to convert unstructured narratives into structured representations of key patient experience dimensions. Furthermore, we evaluated whether Large Language Models (LLMs) can automatically extract information using this schema. Our findings advance scalable, patient-centered approaches to chronic pain assessment, paving the way for more effective, data-driven management strategies.

Capturing Patients' Lived Experiences with Chronic Pain through Motivational Interviewing and Information Extraction

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.

Explainability for NLP in Pharmacovigilance: A Study on Adverse Event Report Triage in Swedish

The healthcare industry has accumulated vast amounts of clinical data, much of which has traditionally been unstructured, including medical records, clinical data, patient communications, and visit notes. Clinician-patient conversations form a crucial part of medical records, with the resulting medical note serving as the ground truth for future interactions and treatment plans. Generating concise and accurate SOAP notes is critical for quality patient care and is especially challenging in specialty care, where relevance, clarity, and adherence to clinician preferences are paramount. These requirements make general-purpose LLMs unsuitable for producing high-quality specialty notes. While recent LLMs like GPT-4 and Sonnet 3.5 have shown promise, their high cost, size, latency, and privacy issues remain barriers for many healthcare providers.

We introduce SpecialtyScribe, a modular pipeline for generating specialty-specific medical notes. It features three components: an Information Extractor to capture relevant data, a Context Retriever to verify and augment content from transcripts, and a Note Writer to produce high quality notes. Our framework and in-house models outperform similarly sized open-source models by over 12% on ROUGE metrics.
Additionally, these models match top closed-source LLMs' performance while being under 1% of their size. We specifically evaluate our framework for oncology, with the potential for adaptation to other specialties.

SpecialtyScribe: Enhancing SOAP note Scribing for Medical Specialties using LLM's

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.

Using LLMs to improve RL policies in personalized health adaptive interventions

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.

Leveraging External Knowledge Bases: Analyzing Presentation Methods and Their Impact on Model Performance

The remarkable results shown by medical question-answering systems lead to their adoption in real-life applications. The systems, however, may misinform the users, even when drawing on scientific evidence to ground the results. The quality of the answers may be verified by the users if they analyze the evidence provided by the systems. User interfaces play an important role in engaging the users. While studies of the user interfaces for biomedical literature search and clinical decision support are abundant, little is known about users' interactions with medical question answering systems and the impact of these systems on health-related decisions. In a study
of several different user interface layouts, we found that only a small number of participants
followed the links to verify automatically generated answers, independently of the interface design. The users who followed the links made better health-related decisions.

Will Gen Z users look for evidence to verify QA System-generated answers?

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.

LLM Based Efficient CSR Summarization using Structured Fact Extraction and Feedback

decision support are abundant, little is known about users' interactions with medical question answering systems and the impact of these systems on health-related decisions. In a study of several different user interface layouts, we found that only a small number of participants followed the links to verify automatically generated answers, independently of the interface design. The users who followed the links made better health-related decisions.

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Explainable ICD Coding via Entity Linking

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Forecasting Clinical Risk from Textual Time Series: Structuring Narratives for Temporal AI in Healthcare

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