Table of Contents

• Exploring Robustness in Doctor-Patient Conversation Summarization: An Analysis of Out-of-Domain SOAP NotesYu-Wen Chen, Julia Hirschberg. 1-9 [doi]
• Efficient Medical Question Answering with Knowledge-Augmented Question GenerationJulien Khlaut, Corentin Dancette, Elodie Ferreres, Alaedine Bennani, Paul Hérent, Pierre Manceron. 10-20 [doi]
• Gemini Goes to Med School: Exploring the Capabilities of Multimodal Large Language Models on Medical Challenge Problems & HallucinationsAnkit Pal, Malaikannan Sankarasubbu. 21-46 [doi]
• Retrieval augmented text-to-SQL generation for epidemiological question answering using electronic health recordsAngelo Ziletti, Leonardo D'Ambrosi. 47-53 [doi]
• ClinicalMamba: A Generative Clinical Language Model on Longitudinal Clinical NotesZhichao Yang 0001, Avijit Mitra, Sunjae Kwon, Hong Yu 0001. 54-63 [doi]
• Working Alliance Transformer for Psychotherapy Dialogue ClassificationBaihan Lin, Guillermo A. Cecchi, Djallel Bouneffouf 0001. 64-69 [doi]
• Building A German Clinical Named Entity Recognition System without In-domain Training DataSiting Liang, Daniel Sonntag. 70-81 [doi]
• DAIC-WOZ: On the Validity of Using the Therapist's prompts in Automatic Depression Detection from Clinical InterviewsSergio Burdisso, Ernesto Reyes-Ramírez, Esaú Villatoro-Tello, Fernando Sánchez-Vega, Adrián Pastor López-Monroy, Petr Motlícek. 82-90 [doi]
• Parameter-Efficient Fine-Tuning of LLaMA for the Clinical DomainAryo Gema, Pasquale Minervini, Luke Daines, Tom Hope, Beatrice Alex. 91-104 [doi]
• A Multilevel Analysis of PubMed-only BERT-based Biomedical ModelsVicente Iván Sánchez Carmona, Shanshan Jiang, Bin Dong 0003. 105-110 [doi]
• A Privacy-Preserving Corpus for Occupational Health in Spanish: Evaluation for NER and Classification TasksClaudio Aracena, Luis Miranda, Thomas Vakili, Fabián Villena, Tamara Quiroga, Fredy Núñez-Torres, Victor Rocco, Jocelyn Dunstan. 111-121 [doi]
• DERA: Enhancing Large Language Model Completions with Dialog-Enabled Resolving AgentsVarun Nair, Elliot Schumacher, Geoffrey J. Tso, Anitha Kannan. 122-161 [doi]
• LlamaMTS: Optimizing Metastasis Detection with Llama Instruction Tuning and BERT-Based Ensemble in Italian Clinical ReportsLivia Lilli, Stefano Patarnello, Carlotta Masciocchi, Valeria Masiello, Fabio Marazzi, Tagliaferri Luca, Nikola Dino Capocchiano. 162-171 [doi]
• Using Structured Health Information for Controlled Generation of Clinical Cases in FrenchHugo Boulanger, Nicolas Hiebel, Olivier Ferret, Karën Fort, Aurélie Névéol. 172-184 [doi]
• Large Language Models Provide Human-Level Medical Text Snippet LabelingIbtihel Amara, Haiyang Yu, Fan Zhang, Yuchen Liu, Benny Li, Chang Liu, Rupesh Kartha, Akshay Goel. 185-195 [doi]
• Conversational Topic Recommendation in Counseling and Psychotherapy with Decision Transformer and Large Language ModelsAylin Gunal, Baihan Lin, Djallel Bouneffouf 0001. 196-201 [doi]
• Leveraging Wikidata for Biomedical Entity Linking in a Low-Resource Setting: A Case Study for GermanFaizan E. Mustafa, Corina Dima, Juan G. Díaz Ochoa, Steffen Staab. 202-207 [doi]
• Revisiting Clinical Outcome Prediction for MIMIC-IVTom Roehr, Alexei Figueroa, Jens-Michalis Papaioannou, Conor Fallon, Keno K. Bressem, Wolfgang Nejdl, Alexander Löser. 208-217 [doi]
• Can LLMs Correct Physicians, Yet? Investigating Effective Interaction Methods in the Medical DomainBurcu Sayin, Pasquale Minervini, Jacopo Staiano, Andrea Passerini. 218-237 [doi]
• Leveraging pre-trained large language models for aphasia detection in English and Chinese speakersYan Cong, Jiyeon Lee, Arianna LaCroix. 238-245 [doi]
• Fusion of Domain-Adapted Vision and Language Models for Medical Visual Question AnsweringCuong Nhat Ha, Shima Asaadi, Sanjeev Kumar Karn, Oladimeji Farri, Tobias Heimann, Thomas A. Runkler. 246-257 [doi]
• LLM-Based Section Identifiers Excel on Open Source but Stumble in Real World ApplicationsSaranya Krishnamoorthy, Ayush Singh, Shabnam Tafreshi. 258-270 [doi]
• Adapting Abstract Meaning Representation Parsing to the Clinical Narrative - the SPRING THYME parserJon Z. Cai, Kristin Wright-Bettner, Martha Palmer, Guergana Savova, James H. Martin. 271-282 [doi]
• SERPENT-VLM : Self-Refining Radiology Report Generation Using Vision Language ModelsManav Nitin Kapadnis, Sohan Patnaik, Abhilash Nandy, Sourjyadip Ray, Pawan Goyal 0002, Debdoot Sheet. 283-291 [doi]
• ERD: A Framework for Improving LLM Reasoning for Cognitive Distortion ClassificationSehee Lim, Yejin Kim, Chi-Hyun Choi, Jy-yong Sohn, Byung-Hoon Kim. 292-300 [doi]
• Leveraging Prompt-Learning for Structured Information Extraction from Crohn's Disease Radiology Reports in a Low-Resource LanguageLiam Hazan, Naama Gavrielov, Roi Reichart, Talar Hagopian, Mary-Louise C. Greer, Ruth Cytter-Kuint, Gili Focht, Dan Turner, Moti Freiman. 301-309 [doi]
• Context Aggregation with Topic-focused Summarization for Personalized Medical Dialogue GenerationZhengyuan Liu, Siti Umairah Md. Salleh, Pavitra Krishnaswamy, Nancy F. Chen. 310-321 [doi]
• Evaluating Lexicon Incorporation for Depression Symptom EstimationKirill Milintsevich, Gaël Dias, Kairit Sirts. 322-328 [doi]
• Semi-automatic Construction of a Word Complexity Lexicon for Japanese Medical TerminologySoichiro Sugihara, Tomoyuki Kajiwara, Takashi Ninomiya, Shoko Wakamiya, Eiji Aramaki. 329-333 [doi]
• TEAM MIPAL at MEDIQA-M3G 2024: Large VQA Models for Dermatological DiagnosisHyeonjin Kim, Min Kim, Jae Jang, KiYoon Yoo, Nojun Kwak. 334-338 [doi]
• MediFact at MEDIQA-M3G 2024: Medical Question Answering in Dermatology with Multimodal LearningNadia Saeed. 339-345 [doi]
• MediFact at MEDIQA-CORR 2024: Why AI Needs a Human TouchNadia Saeed. 346-352 [doi]
• KnowLab_AIMed at MEDIQA-CORR 2024: Chain-of-Though (CoT) prompting strategies for medical error detection and correctionZhaolong Wu, Abul Hasan, Jinge Wu, Yunsoo Kim, Jason Pui Yin Cheung, Teng Zhang, Honghan Wu. 353-359 [doi]
• PromptMind Team at EHRSQL-2024: Improving Reliability of SQL Generation using Ensemble LLMsSatya Kesav Gundabathula, Sriram R. Kolar. 360-366 [doi]
• PromptMind Team at MEDIQA-CORR 2024: Improving Clinical Text Correction with Error Categorization and LLM EnsemblesSatya Kesav Gundabathula, Sriram R. Kolar. 367-373 [doi]
• Maven at MEDIQA-CORR 2024: Leveraging RAG and Medical LLM for Error Detection and Correction in Medical NotesSuramya Jadhav, Abhay Shanbhag, Sumedh Joshi, Atharva Date, Sheetal Sonawane. 374-381 [doi]
• LAILab at Chemotimelines 2024: Finetuning sequence-to-sequence language models for temporal relation extraction towards cancer patient undergoing chemotherapy treatmentShohreh Haddadan, Tuan-Dung Le, Thanh Duong, Thanh Thieu. 382-393 [doi]
• Lexicans at Chemotimelines 2024: Chemotimeline Chronicles - Leveraging Large Language Models (LLMs) for Temporal Relations Extraction in Oncological Electronic Health RecordsVishakha Sharma, Andres Fernandez, Andrei Ioanovici, David Talby, Frederik Buijs. 394-405 [doi]
• Team NLPeers at Chemotimelines 2024: Evaluation of two timeline extraction methods, can generative LLM do it all or is smaller model fine-tuning still relevant ?Nesrine Bannour, Judith Jeyafreeda Andrew, Marc Vincent. 406-416 [doi]
• KCLab at Chemotimelines 2024: End-to-end system for chemotherapy timeline extraction - Subtask2Yukun Tan, Merve Dede, Ken Chen 0001. 417-421 [doi]
• Project PRIMUS at EHRSQL 2024 : Text-to-SQL Generation using Large Language Model for EHR AnalysisSourav Joy, Rohan Ahmed, Argha Saha, Minhaj Habil, Utsho Das, Partha Bhowmik. 422-427 [doi]
• NYULangone at Chemotimelines 2024: Utilizing Open-Weights Large Language Models for Chemotherapy Event ExtractionJeff Zhang, Yin Aphinyanaphongs, Anthony Cardillo. 428-430 [doi]
• AIRI NLP Team at EHRSQL 2024 Shared Task: T5 and Logistic Regression to the RescueOleg Somov, Alexey Dontsov, Elena Tutubalina. 431-438 [doi]
• IKIM at MEDIQA-M3G 2024: Multilingual Visual Question-Answering for Dermatology through VLM Fine-tuning and LLM TranslationsMarie Bauer, Constantin Seibold, Jens Kleesiek, Amin Dada. 439-447 [doi]
• NEUI at MEDIQA-M3G 2024: Medical VQA through consensusRicardo García, Oscar Lithgow-Serrano. 448-460 [doi]
• VerbaNexAI at MEDIQA-CORR: Efficacy of GRU with BioWordVec and ClinicalBERT in Error Correction in Clinical NotesJuan Pajaro, Edwin Puertas, David Villate, Laura Estrada, Laura Tinjaca. 461-469 [doi]
• HSE NLP Team at MEDIQA-CORR 2024 Task: In-Prompt Ensemble with Entities and Knowledge Graph for Medical Error CorrectionAirat Valiev, Elena Tutubalina. 470-482 [doi]
• Wonder at Chemotimelines 2024: MedTimeline: An End-to-End NLP System for Timeline Extraction from Clinical NarrativesLiwei Wang 0010, Qiuhao Lu, Rui Li, Sunyang Fu, Hongfang Liu. 483-487 [doi]
• Edinburgh Clinical NLP at MEDIQA-CORR 2024: Guiding Large Language Models with HintsAryo Gema, Chaeeun Lee, Pasquale Minervini, Luke Daines, T. Ian Simpson, Beatrice Alex. 488-501 [doi]
• UMass-BioNLP at MEDIQA-M3G 2024: DermPrompt - A Systematic Exploration of Prompt Engineering with GPT-4V for Dermatological DiagnosisParth Vashisht, Abhilasha Lodha, Mukta Maddipatla, Zonghai Yao, Avijit Mitra, Zhichao Yang 0001, Sunjae Kwon, Junda Wang, Hong Yu 0001. 502-525 [doi]
• KU-DMIS at MEDIQA-CORR 2024: Exploring the Reasoning Capabilities of Small Language Models in Medical Error CorrectionHyeon Hwang, Taewhoo Lee, Hyunjae Kim, Jaewoo Kang. 526-536 [doi]
• CLD-MEC at MEDIQA- CORR 2024 Task: GPT-4 Multi-Stage Clinical Chain of Thought Prompting for Medical Errors Detection and CorrectionRenad Alzghoul, Ayaabdelhaq Ayaabdelhaq, Abdulrahman Tabaza, Ahmad Altamimi. 537-556 [doi]
• Overview of the 2024 Shared Task on Chemotherapy Treatment Timeline ExtractionJiarui Yao, Harry Hochheiser, Wonjin Yoon, Eli Goldner, Guergana Savova. 557-569 [doi]
• IryoNLP at MEDIQA-CORR 2024: Tackling the Medical Error Detection & Correction Task on the Shoulders of Medical AgentsJean-Philippe Corbeil. 570-580 [doi]
• Overview of the MEDIQA-M3G 2024 Shared Task on Multilingual Multimodal Medical Answer GenerationWen-wai Yim, Asma Ben Abacha, Yujuan Fu, Zhaoyi Sun, Fei Xia, Meliha Yetisgen, Martin Krallinger. 581-589 [doi]
• EM_Mixers at MEDIQA-CORR 2024: Knowledge-Enhanced Few-Shot In-Context Learning for Medical Error Detection and CorrectionSwati Rajwal, Eugene Agichtein, Abeed Sarker. 590-595 [doi]
• Overview of the MEDIQA-CORR 2024 Shared Task on Medical Error Detection and CorrectionAsma Ben Abacha, Wen-wai Yim, Yujuan Fu, Zhaoyi Sun, Fei Xia, Meliha Yetisgen. 596-603 [doi]
• UTSA-NLP at ChemoTimelines 2024: Evaluating Instruction-Tuned Language Models for Temporal Relation ExtractionXingmeng Zhao, Anthony Rios. 604-615 [doi]
• WangLab at MEDIQA-CORR 2024: Optimized LLM-based Programs for Medical Error Detection and CorrectionAugustin Toma, Ronald Xie, Steven Palayew, Patrick R. Lawler, Bo Wang 0044. 616-623 [doi]
• WangLab at MEDIQA-M3G 2024: Multimodal Medical Answer Generation using Large Language ModelsAugustin Toma, Ronald Xie, Steven Palayew, Gary D. Bader, Bo Wang 0044. 624-634 [doi]
• LG AI Research & KAIST at EHRSQL 2024: Self-Training Large Language Models with Pseudo-Labeled Unanswerable Questions for a Reliable Text-to-SQL System on EHRsYongrae Jo, Seongyun Lee, Minju Seo, Sung Ju Hwang, Moontae Lee. 635-643 [doi]
• Overview of the EHRSQL 2024 Shared Task on Reliable Text-to-SQL Modeling on Electronic Health RecordsGyubok Lee, Sunjun Kweon, Seongsu Bae, Edward Choi. 644-654 [doi]
• Saama Technologies at EHRSQL 2024: SQL Generation through Classification Answer Selector by LLMMohammed Jabir, Kamal Raj Kanakarajan, Malaikannan Sankarasubbu. 655-671 [doi]
• KU-DMIS at EHRSQL 2024 : Generating SQL query via question templatization in EHRHajung Kim, Chanhwi Kim, Hoonick Lee, Kyochul Jang, Jiwoo Lee, Kyungjae Lee 0002, Gangwoo Kim, Jaewoo Kang. 672-686 [doi]
• ProbGate at EHRSQL 2024: Enhancing SQL Query Generation Accuracy through Probabilistic Threshold Filtering and Error HandlingSangRyul Kim, Donghee Han, Sehyun Kim. 687-696 [doi]
• LTRC-IIITH at EHRSQL 2024: Enhancing Reliability of Text-to-SQL Systems through Abstention and Confidence ThresholdingJerrin Thomas, Pruthwik Mishra, Dipti Misra Sharma, Parameswari Krishnamurthy. 697-702 [doi]
• LTRC-IIITH at MEDIQA-M3G 2024: Medical Visual Question Answering with Vision-Language ModelsJerrin Thomas, Sushvin Marimuthu, Parameswari Krishnamurthy. 703-707 [doi]