United States

The rapid expansion of biomedical literature makes manual identification of novel drug-disease relationships increasingly difficult. Existing approaches have leveraged LLMs to mine abstracts or construct knowledge graphs for drug repurposing. There are two key limitations: finite context windows for capturing macro-level research trends, and single-pass black-box pipelines make it difficult to verify outputs. This paper proposes a pipeline for discovering new drug targets by combining disease and drug research trends using Large Language Models (LLMs). Our method extracts PICO components from PubMed abstracts, normalizing the Population and Intervention Component to ICD and ATC codes, respectively. A temporal frequency delta matrix is constructed to capture publication count shifts across 2013 to 2022, then used to discover novel drug areas. Compared with the abstract-based baseline, our approach showed qualitative signs of generating combinations that were more closely aligned with observed research trends and, in some cases, more clinically plausible. These findings suggest the potential usefulness of structured trend information for LLM-based exploration, although the differences between the two methods were limited and the results remain preliminary. Future work will focus on validating the consistency and reliability of these candidates.

ACL 2026

Fast, Accurate, and Local Conversion of MIMIC-IV to OMOP with DBT

common data model

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21st edition of EANN (International Conference on Engineering Applications of Neural Networks) should be held in Halkidiki, Greece. Due to current Covid-19 situation the conference will be held virtually and Underline will host a repository of video talks and preserve them for the future. Videos will be released on Underline one day after the conference. 

EANN 2020

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It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) <br><br><br>
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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

Family health history (FHx) offers insight into a person's health and disease risk, but it is largely held within free-text clinical formats that require processing for maximal utility of the data. The rapid deployment of ambient AI scribes and conversational agents in clinical settings necessitates evaluation on dynamic patient-clinician and patient-agent dialogs. To address this gap, we introduce two new datasets of patient FHx dialog documents designed to benchmark information extraction and entity linking. Distinct from clinician-entered datasets, patient-reported dialog data has its own semantic and content characteristics, which need to be studied for more patient-centered healthcare. We contribute a publicly available resource called FHexchange, with new annotations for family members, clinical observations, related entities, and standardized UMLS CUIs, offering the clinical NLP community a robust evaluation bed for emerging generative AI tools.

Exploring Novel Drug Research Area using Large Language Models Based on Research Trends in Biomedical Literature

We present IndicMedDialog, a parallel multi-turn medical dialogue dataset spanning English and nine Indic languages (Assamese, Bengali, Gujarati, Hindi, Marathi, Punjabi, Tamil, Telugu, and Urdu). The dataset extends the MDDial corpus with LLM-generated synthetic consultations, translated using TranslateGemma, verified by native speakers, and refined through a script-aware post-processing pipeline to correct phonetic, lexical, and character-spacing errors introduced during automatic translation. Building on this dataset, we fine-tune IndicMedLM via parameter-efficient adaptation (LoRA) of a quantized small language model, incorporating an optional patient pre-context to personalise multi-turn symptom elicitation. We evaluate IndicMedLM against zero-shot multilingual baselines across ten languages and conduct systematic error analysis, identifying five failure modes: Instruction Drift, Label Collapse, Cross-Domain Confusion, Tokenization Failure, and Paraphrase-over-Label Generation. Results show strong post-processed diagnostic accuracy in Hindi, Marathi, and Bengali, while Assamese, Tamil, and Telugu remain in an extreme failure tier attributable to base-model tokenizer gaps, a finding with direct patient safety implications. Medical expert evaluation confirms the clinical plausibility and safety of the generated consultations.

Forgotten Words: Benchmarking NeoBERT for Dementia Detection in Low-Resource Conversational Filipino and English Speech

Three-dimensional Computed Tomography (3D CT) is a cornerstone of precision medicine. Most AI diagnostic models analyze large num bers of CTslices uniformly, treating all slices as equally important. While this has partly accel erated radiologists'workflows, it overlooks that clinically relevant information is often sparsely distributed throughout a volume. Without tar geted or weighted processing, fine-grained cues may be missed and substantial computation wasted on diagnostically uninformative slices. Wepropose aradiologist-simulating framework for selective and efficient 3D CT interpreta tion. Evaluated on a 3D CT dataset covering eight thoracic lesion types, it was compared with state-of-the-art multimodal large language models such as GPT-4o and supervised visual backbones including ViT and ResNet-50. Us ing accuracy, F1-score, AUC, and blind radiolo gist assessment, Screen-CLIP achieved an AUC of 0.87 and F1-score of 0.82, surpassing ViT Base (AUC: 0.84). For report generation, our method outperformed M3D across all metrics, reaching a BLEU-Avg of 29.03, and achieved the highest average Doctors' Score (6.16/10) in a preliminary human evaluation.

IndicMedDialog: A Parallel Multi-Turn Medical Dialogue Dataset for Accessible Healthcare in Indic Languages

This paper presents an overview of the ClinicalSkillQA 2026 shared task, which was organized with the BioNLP Workshop at ACL 2026. The goal of this shared task is to evaluate continuous perception and procedural reasoning in clinical skill assessment by requiring systems to reconstruct the correct temporal order of shuffled clinical key frames and generate rationales grounded in clinical workflow knowledge. The benchmark contains 200 test-only instances sampled from clinical skill videos, covering three emergency-care procedures. Each instance is annotated with the ground-truth temporal order and an expert-verified rationale. A total of seven teams participated in the task, collectively making 90 submissions, with four teams providing system description papers. Systems are evaluated using Task Accuracy, Pairwise Accuracy, and BERTScore, which measure exact sequence reconstruction, local temporal consistency, and rationale quality, respectively. In this paper, we describe the task setup, dataset construction, and evaluation criteria. We further summarize the methodologies adopted by participating teams and present a comprehensive analysis of the submitted systems. The official results suggest that current models still struggle with continuous perception and procedural reasoning, especially when they must integrate visual evidence, temporal structure, and clinical workflow knowledge.

Overview of the MedGenVidQA 2026 Shared Task on Medical Generative Video Question Answering

We build an ensemble of 10 transformer encoders for the MedExACT 2026 shared task on medical decision span detection. The ensemble is diversified along three training directions: encoder initialization (including domain-adaptive pre-training on clinical text), loss function, and data augmentation with LLM-generated synthetic notes and silver-labeled clinical documents. Greedy forward search selects the combination with the highest validation final score. A BERT-based boundary refiner is applied to the ensemble's predicted spans to correct offset errors before submission.

Otter at MedExAct2026: Diverse Encoder Ensemble for Medical Decision Span Detection

We present our system for the PsyDefDetect shared task, which focuses on detecting and classifying psychological defense mechanisms in peer emotional support conversations. Our core contribution is a hierarchical classification framework that structures prediction as a coarse-to-fine pipeline over a clinically validated label 
 hierarchy, grounded in the Defense Mechanism Rating Scales (DMRS). Through systematic experimentation with flat fine-tuning, few-shot prompting, and hierarchical classification, we demonstrate that explicitly modelling the structured relationships among defense levels offers a more effective alternative to flat classification, achieving a macro F1 of 0.23 on the official test set.

KCL-Cogstack at PsyDefDetect: A Hierarchical Approach to Detecting Defense Mechanisms in Supportive Dialogue

This paper presents an ensemble of Qwen3.5-4B language models for extracting medical decisions from discharge summaries in the MedDec dataset. The models were trained to annotate discharge summaries with inline XML-like tags. Three different training strategies were used including dynamic fine-tuning, reinforcement learning, and pseudo-label augmentation. By combining predictions based on inter-model agreement, the system improved performance across evaluation metrics, achieving an overall F1 of 0.5942 and ranking second on the test leaderboard. The results also showed stable performance across demographic groups, suggesting fairness for underrepresented populations.

LAMAR at MedExACT 2026: Agreement-Driven Large Language Model Ensembles for Clinical Decision Extraction from Discharge Summaries

Extracting structured medical decisions from
 ICU discharge summaries is hard because of
 long documents, severe category imbalance
 across nine DICTUM decision types, and a
 fairness-aware evaluation that penalizes incon-
 sistent performance across demographic sub-
 groups. We present our system for the MedEx-
 ACT 2026 shared task (Elgaar et al., 2026),
 which fine-tunes BiomedBERT with a com-
 posite loss combining label-smoothed cross-
 entropy, a soft token-F1 auxiliary term, and
 R-Drop regularization. At inference time we
 apply a deterministic ensemble: half-offset
 sliding-window augmentation across four win-
 dow configurations, dual-branch logit aggrega-
 tion from the same checkpoint, per-category
 length calibration on the Anchor Branch, and
 sparse routing of categories 4 and 7 to a context-
 weighted specialist branch motivated by their
 unusual span-length distributions. Adding R-
 Drop improved validation Overall_F1 by 1.24
 points over the CE + soft-F1 baseline, with a
 larger 1.70-point gain on Worst-Group F1. Our
 best submission achieves Span F1 of 0.4900,
 Token F1 of 0.6796, and an official Overall_F1
 of 0.5724, with the African American subgroup
 as the Worst-Group bottleneck at Base_Score
 0.5601

Sparse Category Routing and Fairness-Aware Optimization for Medical Decision Extraction

This paper presents an approach to localizing visual answers within continuous medical videos using a multi-step multimodal generation pipeline with the MedGenVidQA dataset. We frame visual answer localization as a multimodal fusion problem, integrating raw video, timestamped ASR transcripts, and VLM-generated scene descriptions into structured contextual blocks, enabling the model to cross-reference spoken commentary against observable physical events. We show that targeted guidance, which forces the model to treat audio transcripts as supplementary hints with observable visual movements, significantly outperforms baseline approaches. It achieves state-of-the-art performance on the test leaderboard, yielding an mIoU of 79.55, alongside IoU@0.3, IoU@0.5, and IoU@0.7 scores of 93.75, 90.00, and 77.50, respectively. Our findings highlight the effectiveness of combining multimodal context fusion with targeted guidance to overcome text bias, establishing a promising approach for achieving the micro-level precision required in the medical domain. We release our code on GitHub at https://github.com/biodatlab/medgenvidqa-lamar.

LAMAR-2 at MedGenVidQA 2026: Visual Answer Localization in Medical Videos via Multimodal LLM and Context-Augmented Prompting

This paper presents a system for Task A of the MedGenVidQA 2026 shared task, which requires simultaneously retrieving relevant PubMed documents and medical videos for 60 consumer health topics. The core contribution is a unified multi-stage pipeline that treats video and document retrieval as complementary rather than independent problems.
 For video retrieval, the system fine-tunes a PubMedBERT bi-encoder on 2,710 MedVidQA training samples using BM25-driven hard negative mining. Video transcripts (833 unique videos) are segmented into overlapping 30-second temporal chunks with a 10-second stride, producing 32,489 indexed chunks. At query time, T5-based query expansion generates enriched queries for BM25 sparse retrieval, while the original query drives FAISS dense retrieval. The two ranked lists are fused via weighted Reciprocal Rank Fusion (RRF, dense weight 0.75, sparse weight 0.25), and a cross-encoder (MiniLM-L-6-v2) re-ranks the top-200 fused candidates to produce the final top-10 videos. For document retrieval, the NCBI PubMed ESearch API is queried using a progressive keyword fallback chain with exponential backoff, ensuring full topic coverage.
 The system achieves a MAP of 0.3898, Recall@10 of 0.8449, and NDCG@10 of 0.1079, with complete 60/60 topic coverage across both retrieval modalities. Key limitations include reliance solely on transcript text for video retrieval (no visual or audio features) and dependence on a live API for document retrieval.

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