Natural language processing (NLP) is a key technology to extract important patient information from clinical narratives to support healthcare applications. The rapid development of large language models (LLMs) has revolutionized many NLP tasks in the clinical domain, yet their optimal use in patient information extraction tasks requires further exploration. This study examines LLMs' effectiveness in patient information extraction, focusing on LLM architectures, fine-tuning strategies, and multi-task instruction tuning techniques for developing robust and generalizable patient information extraction systems. This study aims to explore key concepts of using LLMs for clinical concept and relation extraction tasks, including: (1) encoder-only or decoder-only LLMs, (2) prompt-based parameter-efficient fine-tuning (PEFT) algorithms, and (3) multi-task instruction tuning on few-shot learning performance. We benchmarked a suite of LLMs, including encoder-based LLMs (BERT, GatorTron) and decoder-based LLMs (GatorTronGPT, Llama 3.1, GatorTronLlama), across five datasets. We compared traditional full-size fine-tuning and prompt-based PEFT. We explored a multi-task instruction tuning framework that combines both tasks across four datasets to evaluate the zero-shot and few-shot learning performance using the leave-one-dataset-out strategy.
PaniniQA: Enhancing Patient Education Through Interactive Question Answering
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Emory University（埃默里大学）；Northeastern University（东北大学）；UMass Chan Medical School（马萨诸塞大学陈医学院）；University of Minnesota（明尼苏达大学）；University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）
Span-level Emotion-Cause-Category Triplet Extraction with Instruction Tuning LLMs and Data Augmentation
College of Computer Science and Engineering（计算机科学与工程学院）；Shandong University of Science and Technology（山东科技大学）；Guangxi Key Lab of Human-machine Interaction and Intelligent Decision（广西人机交互与智能决策重点实验室）；Nanning Normal University（南宁师范大学）
InstructIE: A Bilingual Instruction-based Information Extraction Dataset
Zhejiang University（浙江大学）；Zhejiang University - Ant Group Joint Laboratory of Knowledge Graph（浙江大学-蚂蚁集团知识图谱联合实验室）；Ant Group（蚂蚁集团）；University of Edinburgh（爱丁堡大学）
From Weak Task Specifications to Scientific Extraction Agents: Optimizing Task Construction
University of Science and Technology of China（中国科学技术大学）；State Key Laboratory of Cognitive Intelligence（认知智能全国重点实验室）；CCCC Second Highway Consultants Co., Ltd.（中交第二公路勘察设计研究院有限公司）
MedReadCtrl: Personalizing medical text generation with readability-controlled instruction learning
Center for Healthcare Organization and Implementation Research（医疗组织与实施研究中心）；VA Bedford Health Care（贝福德医疗中心）；Miner School of Computer and Information Sciences（米纳尔计算机与信息科学学院）；University of Massachusetts Medical School（马萨诸塞大学医学学院）；School of Nursing（护理学院）；Zuckerberg College of Health Sciences（泽克伯格健康科学学院）
Dutch Metaphor Extraction from Cancer Patients' Interviews and Forum Data using LLMs and Human in the Loop
Biomedical Data Sciences, Leiden University Medical Center（莱顿大学医学中心生物医学数据科学）；Leiden Institute of Advanced Computer Science (LIACS), Leiden University（莱顿大学先进计算机科学研究所）；Department of Radiation Oncology (MAASTRO), GROW Research Institute for Oncology（肿瘤学研究与生殖学研究所放射肿瘤科）
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）
Not All Documents Are What You Need for Extracting Instruction Tuning Data
Department of Computer Science, Beijing Institute of Technology, Beijing, China（北京理工大学计算机科学系）；Sensetime Research, Shenzhen, China（深睿时代研究）；Shanghai Artificial Intelligence Laboratory, Shanghai, China（上海人工智能实验室）；University of Arizona, USA（亚利桑那大学）；Meta, USA（Meta）
Beyond Prompt: Fine-grained Simulation of Cognitively Impaired Standardized Patients via Stochastic Steering
School of Software, Xi’an Jiaotong University（西安交通大学软件学院）；Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究所）；College of Computer Science, Sichuan University（四川大学计算机学院）；Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education, China（教育部机器学习与工业智能工程研究中心，中国）
EPPCMinerBen: A Novel Benchmark for Evaluating Large Language Models on Electronic Patient-Provider Communication via the Patient Portal
Department of Emergency Medicine, Yale School of Medicine（耶鲁医学院急诊医学部）；Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland Clinic（克利夫兰医学中心Lerner学院（凯斯西储大学））；Medical Oncology, Yale School of Medicine（耶鲁医学院肿瘤学部）
Supervised Fine Tuning of Large Language Models for Domain Specific Knowledge Graph Construction:A Case Study on Hunan's Historical Celebrities
College of Information Science and Engineering（信息科学与工程学院）；Hunan Provincial Key Laboratory of Philosophy and Social Sciences of Yuelushan Cultural and Digital Communication (Artificial Intelligence and International Communication AIIC)（湖南省级哲学社会科学岳麓文化与数字传播重点实验室（人工智能与国际传播AIIC））；College of Computer Science and Electronic Engineering（计算机科学与电子工程学院）
Large Language Model-based Role-Playing for Personalized Medical Jargon Extraction
Manning College of Information and Computer Sciences, University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校曼宁信息与计算机科学学院）；Mendoza College of Business, University of Notre Dame（圣母大学门多萨商学院）；University of Massachusetts Medical School（马萨诸塞大学医学院）；Miner School of Computer and Information Sciences, University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校迈纳计算机与信息科学学院）；VA Bedford Health Care（贝德福德退伍军人事务医疗中心）
TEAR: Table Extraction with Attribute Recommendation from Texts via Large Language Models
Hong Kong University of Science and Technology（香港科技大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；South China Normal University（华南师范大学）
Retrieval-Augmented and Knowledge-Grounded Language Models for Faithful Clinical Medicine
University of Oxford（牛津大学）；Peking University（北京大学）；Yale University（耶鲁大学）；Tencent（腾讯）；Oxford-Suzhou Centre for Advanced Research（牛津-苏州高等研究院）；Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）
The University of Tokyo（东京大学）；National Institute of Informatics（国立信息学研究所）；Kyoto University（京都大学）
An interactive instruction following task has been proposed as a benchmark for learning to map natural language instructions and first-person vision into sequences of actions to interact with objects in 3D environments. We found that an existing end-to-end neural model for this task tends to fail to interact with objects of unseen attributes and follow various instructions. We assume that this problem is caused by the high sensitivity of neural feature extraction to small changes in vision and language inputs. To mitigate this problem, we propose a neuro-symbolic approach that utilizes high-le…
Extracting Social Determinants of Health from Pediatric Patient Notes Using Large Language Models: Novel Corpus and Methods
University of Washington（华盛顿大学）；George Mason University（乔治梅森大学）；Dana-Farber Cancer Institute（达纳-法伯癌症研究所）；Boston Children's Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）
LLMs-based Few-Shot Disease Predictions using EHR: A Novel Approach Combining Predictive Agent Reasoning and Critical Agent Instruction
Emory University（埃默里大学）；University of Washington（华盛顿大学）；UCSD（加利福尼亚大学圣迭戈分校）；Rollins School of Public Health, Emory University（埃默里大学罗林斯公共卫生学院）；School of Medicine, Emory University（埃默里大学医学院）
LLMs for Medical Consultation Are Evaluated Too Late: The Preformulation Gap
Harvard T.H. Chan School of Public Health（哈佛陈曾熙公共卫生学院）；Harvard Medical School（哈佛医学院）；Australian Artificial Intelligence Institute, University of Technology Sydney（悉尼科技大学澳大利亚人工智能研究所）；Raycaster；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）
