Small Models are LLM Knowledge Triggers on Medical Tabular Prediction
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Thrust of Artificial Intelligence, Information Hub, HKUST (GZ)（香港科技大学（广州）信息枢纽人工智能方向）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；The Second Affiliated Hospital Zhejiang University School of Medicine（浙江大学医学院附属第二医院）；Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence（浙江省医学影像人工智能重点实验室）
Recent development in large language models (LLMs) has demonstrated impressive domain proficiency on unstructured textual or multi-modal tasks. However, despite with intrinsic world knowledge, their application on structured tabular data prediction still lags behind, primarily due to the numerical insensitivity and modality discrepancy that brings a gap between LLM reasoning and statistical tabular learning. Unlike textual or vision data (e.g., electronic clinical notes or medical imaging data), tabular data is often presented in heterogeneous numerical values (e.g., CBC reports). This ubiquitous data format requires intensive expert annotation, and its numerical nature limits LLMs' capability to effectively transfer untapped domain expertise. In this paper, we propose SERSAL, a general self-prompting method by synergy learning with small models to enhance LLM tabular prediction in an unsupervised manner. Specifically, SERSAL utilizes the LLM's prior outcomes as original soft noisy annotations, which are dynamically leveraged to teach a better small student model. Reversely, the outcomes from the trained small model are used to teach the LLM to further refine its real capability. This process can be repeatedly applied to gradually distill refined knowledge for continuous progress. Comprehensive experiments on widely used medical domain tabular datasets show that, without access to gold labels, applying SERSAL to OpenAI GPT reasoning process attains substantial improvement compared to linguistic prompting methods, which serves as an orthogonal direction for tabular LLM, and increasing prompting bonus is observed as more powerful LLMs appear.
When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications?
University of Colorado（科罗拉多大学）；University of Wisconsin Madison（威斯康星大学麦迪逊分校）；Mass General Brigham, Harvard Medical School（哈佛医学院麻省总医院）；Boston Children Hospital, Harvard Medical School（哈佛医学院波士顿儿童医院）；Loyola University Chicago（芝加哥洛约拉大学）；Dana-Farber Cancer Institute（达纳-法伯癌症研究所）
P-Transformer: A Prompt-based Multimodal Transformer Architecture For Medical Tabular Data
Saw Swee Hock School of Public Health, National University of Singapore（新加坡国立大学苏瑞福公共卫生学院）；Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究院）；Singapore General Hospital（新加坡综合医院）
Evaluating LLM Abilities to Understand Tabular Electronic Health Records: A Comprehensive Study of Patient Data Extraction and Retrieval
Université Paul Sabatier（保罗·萨巴蒂埃大学）；IRIT（信息论与电信研究所）；Centre Hospitalier Universitaire de Toulouse（图卢兹大学医院中心）；INSERM（法国国家健康与医学研究院）；Université de Toulouse（图卢兹大学）；Faculté de Médecine（医学院）
Large Language Models versus Classical Machine Learning: Performance in COVID-19 Mortality Prediction Using High-Dimensional Tabular Data
Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学胃肠病与肝病研究所）；Faculty of Medicine, Isfahan University of Medical Sciences（伊斯法罕医科大学医学院）；Faculty of Business and Information Technology, Ontario Tech University（安大略理工大学商业与信息技术学院）；School of Medicine, Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学医学院）；Digestive Disease Research Institute, Tehran University of Medical Sciences（德黑兰医科大学消化疾病研究所）；Department of Surgery, The Johns Hopkins University（约翰霍普金斯大学外科学系）；Student Research Committee, School of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学护理与助产学院学生研究委员会）；MPH department, Shiraz University of Medical Sciences（设拉子医科大学公共卫生硕士系）；Department of Emergency Medicine, School of Medicine, Safety Promotion and Injury Prevention Research Center, Imam Hossein Hospital, Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学伊玛目侯赛因医院医学院急诊医学系安全促进与伤害预防研究中心）；Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center（西达赛奈医疗中心卡什胃肠病与肝病科）；Department of Medicine, Stanford University（斯坦福大学医学系）；Division of Data Driven and Digital Health (D3M), The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院查尔斯·布朗夫曼个性化医学研究所数据驱动与数字健康部）；Infectious Diseases and Tropical Medicine Research Center, Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学传染病与热带医学研究中心）；Department of Infectious Diseases, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学洛格曼·哈基姆医院传染病科）；National Institute for Health and Care Research (NIHR), Nottingham Biomedical Research Centre, Hearing Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham（诺丁汉大学医学院国家健康与护理研究所诺丁汉生物医学研究中心听力科学、心理健康与临床神经科学）
Calibrated? Not for Everyone: How Sexual Orientation and Religious Markers Distort LLM Accuracy and Confidence in Medical QA
Department of Medical Informatics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands（阿姆斯特丹大学医学信息学系，阿姆斯特丹大学医学中心，阿姆斯特丹，荷兰）；Amsterdam Public Health, Methodology, Amsterdam, The Netherlands（阿姆斯特丹公共卫生，方法学，阿姆斯特丹，荷兰）
A Case Study on Pros and Cons of Regular Expression Detection and Dependency Parsing for Negation Extraction from German Medical Documents. Technical Report
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Oldenburg University（奥尔登堡大学）
