Large Language Multimodal Models for 5-Year Chronic Disease Cohort Prediction Using EHR Data
School of Systems and Enterprises, Stevens Institute of Technology（史蒂文斯理工学院系统与企业学院）；National Yang Ming Chiao Tung University（国立阳明交通大学）；Institute of Hospital and Health Care Administration, National Yang Ming Chiao Tung University（国立阳明交通大学医院暨健康保健管理研究所）；School of Computing and Information Science, Florida International University（佛罗里达国际大学计算与信息科学学院）；University of Michigan（密歇根大学）；Far Eastern Memorial Hospital（远东纪念医院）
Chronic diseases such as diabetes are the leading causes of morbidity and mortality worldwide. Numerous research studies have been attempted with various deep learning models in diagnosis. However, most previous studies had certain limitations, including using publicly available datasets (e.g. MIMIC), and imbalanced data. In this study, we collected five-year electronic health records (EHRs) from the Taiwan hospital database, including 1,420,596 clinical notes, 387,392 laboratory test results, and more than 1,505 laboratory test items, focusing on research pre-training large language models. We proposed a novel Large Language Multimodal Models (LLMMs) framework incorporating multimodal data from clinical notes and laboratory test results for the prediction of chronic disease risk. Our method combined a text embedding encoder and multi-head attention layer to learn laboratory test values, utilizing a deep neural network (DNN) module to merge blood features with chronic disease semantics into a latent space. In our experiments, we observe that clinicalBERT and PubMed-BERT, when combined with attention fusion, can achieve an accuracy of 73% in multiclass chronic diseases and diabetes prediction. By transforming laboratory test values into textual descriptions and employing the Flan T-5 model, we achieved a 76% Area Under the ROC Curve (AUROC), demonstrating the effectiveness of leveraging numerical text data for training and inference in language models. This approach significantly improves the accuracy of early-stage diabetes prediction.
Electronic health records (EHR) offer unprecedented opportunities for in-depth clinical phenotyping and prediction of clinical outcomes. Combining multiple data sources is crucial to generate a complete picture of disease prevalence, incidence and trajectories. The standard approach to combining clinical data involves collating clinical terms across different terminology systems using curated maps, which are often inaccurate and/or incomplete. Here, we propose sEHR-CE, a novel framework based on transformers to enable integrated phenotyping and analyses of heterogeneous clinical datasets witho…
CURENet: Combining Unified Representations for Efficient Chronic Disease Prediction
National Yang Ming Chiao Tung University（国立阳明交通大学）；Can Tho University（Cần Thơ 大学）；Stevens Institute of Technology（史蒂文斯理工学院）；University of Michigan（密歇根大学）；Massachusetts Institute of Technology（麻省理工学院）；Florida International University（佛罗里达国际大学）；Southern Utah University（南方犹他大学）；Far Eastern Memorial Hospital（东方纪念医院）；Yuan Ze University（元智大学）
Binary Gaussian Copula Synthesis: an LLM-powered data augmentation framework for early dialysis prediction in chronic kidney disease
Department of Industrial & Management Systems Engineering, West Virginia University（威斯康星大学工业与管理系统工程系）；Department of Electrical and Computer Engineering, Concordia University（康科迪亚大学电气与计算机工程系）；Department of Computer Science, University of California, Davis（加州大学戴维斯分校计算机科学系）；School of Mathematical & Data Sciences, West Virginia University（威斯康星大学数学与数据科学学院）；School of Systems Science and Industrial Engineering, The State University of New York at Binghamton（纽约州立大学布法罗分校系统科学与工业工程学院）；H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology（佐治亚理工学院H.米尔顿·斯图尔特工业与系统工程学院）
AIPatient Arena: EHR-grounded evaluation of large language models in end-to-end clinical consultation workflows
School of Control Science and Engineering, Shandong University（控制科学与工程学院，山东大学）；Key Laboratory of Machine Intelligence and System Control, Shandong University（机器智能与系统控制重点实验室，山东大学）；Department of Medicine and Therapeutics, The Chinese University of Hong Kong（医学与治疗学系，香港中文大学）；Department of Geriatric Medicine, Qilu Hospital of Shandong University（老年医学科，山东大学齐鲁医院）；Department of Psychiatry, The Chinese University of Hong Kong（精神病学系，香港中文大学）；Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong（李秋虹家庭睡眠评估单元，精神病学系，医学院，香港中文大学）；Li Ka Shing Institute of Health Sciences, Faculty of Medicine, The Chinese University of Hong Kong（李嘉诚健康科学研究院，医学院，香港中文大学）；Gerald Choa Neuroscience Institute, Department of Medicine and Therapeutics, The Chinese University of Hong Kong（Gerald Choa 神经科学研究所，医学与治疗学系，香港中文大学）
The Potential of LLMs in Medical Education: Generating Questions and Answers for Qualification Exams
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beijing Friendship Hospital, Capital Medical University（首都医科大学附属北京友谊医院）；Xunfei Healthcare Technology Co., Ltd（讯飞医疗科技有限公司）；School of Information and Communication Engineering, Hainan University（海南大学信息与通信工程学院）；Guangzhou University（广州大学）
Real-world validation of a multimodal LLM-powered pipeline for High-Accuracy Clinical Trial Patient Matching leveraging EHR data
Inato（伊纳托（公司名））；Center for Research in Epidemiology and Statistics (CRESS)（流行病学与统计学研究中心）；Université Paris Cité（巴黎城市大学）；Université Sorbonne Paris Nord（巴黎北索邦大学）；INSERM（法国国家健康与医学研究院）；INRAE（法国国家农业、食品与环境研究院）；AP-HP（巴黎公共医疗集团）；Hôpital Hôtel Dieu（主宫医院）
Tokenization Tradeoffs in Structured EHR Foundation Models
Child Health Evaluative Sciences（儿童健康评估科学）；The Hospital for Sick Children（圣迈克尔医院）；University Health Network（大学健康网络）；Department of Medicine（医学部）；Division of Computational Medicine, Department of Medicine（计算医学科，医学部）；Stanford University（斯坦福大学）
Early detection of preventable diseases is important for better disease management, improved inter-ventions, and more efficient health-care resource allocation. Various machine learning approacheshave been developed to utilize information in Electronic Health Record (EHR) for this task. Majorityof previous attempts, however, focus on structured fields and lose the vast amount of information inthe unstructured notes. In this work we propose a general multi-task framework for disease onsetprediction that combines both free-text medical notes and structured information. We compareperformance of d…
A General Multimodal Probing Framework for Data Fusion and Prediction with Frozen Large Language Models: Evidence from Multimodal EHR Data
Department of Statistics, Pennsylvania State University（宾夕法尼亚州立大学统计学系）；Department of Biostatistics, University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校生物统计学系）；Department of Statistics and Operations Research, University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校统计学与运筹学系）
We apply deep learning-based language models to the task of patient cohort retrieval (CR) with the aim to assess their efficacy. The task of CR requires the extraction of relevant documents from the electronic health records (EHRs) on the basis of a given query. Given the recent advancements in the field of document retrieval, we map the task of CR to a document retrieval task and apply various deep neural models implemented for the general domain tasks. In this paper, we propose a framework for retrieving patient cohorts using neural language models without the need of explicit feature engine…
Mayo Clinic（妙佑医疗国际）；Tufts Clinical and Translational Science Institute（塔夫茨临床与转化科学研究所）；Tufts Medical Center（塔夫茨医疗中心）；University of Kentucky（肯塔基大学）；University of Kansas Medical Center（堪萨斯大学医学中心）；University of Minnesota at Twin Cities（明尼苏达大学双城分校）；University of Florida（佛罗里达大学）；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；University of Michigan Medical School（密歇根大学医学院）；Wake Forest Baptist Medical Center（维克森林浸信会医疗中心）；Johns Hopkins University（约翰斯·霍普金斯大学）；Stony Brook University（石溪大学）；Sage Bionetwork（Sage生物网络）
While we pay attention to the latest advances in clinical natural language processing (NLP), we can notice some resistance in the clinical and translational research community to adopt NLP models due to limited transparency, interpretability, and usability. In this study, we proposed an open natural language processing development framework. We evaluated it through the implementation of NLP algorithms for the National COVID Cohort Collaborative (N3C). Based on the interests in information extraction from COVID-19 related clinical notes, our work includes 1) an open data annotation process usin…
A Method for Characterizing Disease Progression from Acute Kidney Injury to Chronic Kidney Disease
Department of Biomedical Informatics, Columbia University（哥伦比亚大学生物医学信息学系）；Department of Medicine, Division of Nephrology, Columbia University（哥伦比亚大学医学系肾脏病科）；Department of Medicine, Division of General Medicine, Columbia University（哥伦比亚大学医学系全科医学）
Who Benefits From Sinus Surgery? Comparing Generative AI and Supervised Machine Learning for Predicting Surgical Outcomes in Chronic Rhinosinusitis
Department of Computer Science, Purdue University（计算机科学系，普渡大学）；Department of Computer Science, Indiana University Indianapolis（计算机科学系，印第安纳大学印第安纳波利斯分校）；Department of Otolaryngology—Head and Neck Surgery, Indiana University School of Medicine（耳鼻喉科—头颈外科，印第安纳大学医学院）
A Weakly Supervised Transformer for Rare Disease Diagnosis and Subphenotyping from EHRs with Pulmonary Case Studies
Harvard T.H. Chan School of Public Health（哈佛T.H. 洪学校公共卫生学院）；Harvard Medical School（哈佛医学院）；Bentley University（伯顿大学）；Columbia University（哥伦比亚大学）；Boston Children’s Hospital（波士顿儿童医院）；Computational Health Informatics Program（计算健康信息学计划）；Department of Pediatrics（儿科部门）；Brigham and Women’s Hospital（布里奇沃特医院）
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；McGill University（麦吉尔大学）；University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；University of Connecticut（康涅狄格大学）；Uconn Health（康涅狄格大学健康中心）；University of Massachusetts Chan Medical School（马萨诸塞大学陈医学院）；Veterans Affairs Bedford Healthcare System（退伍军人事务部贝德福德医疗系统）；United States Department of Veterans Affairs（美国退伍军人事务部）；University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）
Importance: Social determinants of health (SDOH) are known to be associated with increased risk of suicidal behaviors, but few studies utilized SDOH from unstructured electronic health record (EHR) notes. Objective: To investigate associations between suicide and recent SDOH, identified using structured and unstructured data. Design: Nested case-control study. Setting: EHR data from the US Veterans Health Administration (VHA). Participants: 6,122,785 Veterans who received care in the US VHA between October 1, 2010, and September 30, 2015. Exposures: Occurrence of SDOH over a maximum span of tw…
