Generalized and Transferable Patient Language Representation for Phenotyping with Limited Data
The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）；McGovern Medical School, The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心麦戈文医学院）
The paradigm of representation learning through transfer learning has the potential to greatly enhance clinical natural language processing. In this work, we propose a multi-task pre-training and fine-tuning approach for learning generalized and transferable patient representations from medical language. The model is first pre-trained with different but related high-prevalence phenotypes and further fine-tuned on downstream target tasks. Our main contribution focuses on the impact this technique can have on low-prevalence phenotypes, a challenging task due to the dearth of data. We validate the representation from pre-training, and fine-tune the multi-task pre-trained models on low-prevalence phenotypes including 38 circulatory diseases, 23 respiratory diseases, and 17 genitourinary diseases. We find multi-task pre-training increases learning efficiency and achieves consistently high performance across the majority of phenotypes. Most important, the multi-task pre-training is almost always either the best-performing model or performs tolerably close to the best-performing model, a property we refer to as robust. All these results lead us to conclude that this multi-task transfer learning architecture is a robust approach for developing generalized and transferable patient language representations for numerous phenotypes.
Unsupervised EHR-based Phenotyping via Matrix and Tensor Decompositions
Simula Metropolitan Center for Digital Engineering（西姆拉大都会数字工程中心）；Oslo Metropolitan University（奥斯陆城市大学）；Swammerdam Institute for Life Sciences, University of Amsterdam（阿姆斯特丹大学斯瓦默丹生命科学研究所）
Comparing Rule-Based and Deep Learning Models for Patient Phenotyping
MIT Critical Data, Laboratory for Computational Physiology（麻省理工学院关键数据与计算生理学实验室）；Harvard John A. Paulson School of Engineering and Applied Sciences（哈佛大学约翰·A·保尔森工程与应用科学学院）；Massachusetts Institute of Technology（麻省理工学院）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；Philips Research North America（飞利浦北美研究院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Massachusetts General Hospital（麻省总医院）；Tufts University School of Medicine（塔夫茨大学医学院）；University of Massachusetts（马萨诸塞大学）；Washington University School of Medicine（华盛顿大学医学院）
Interpretable phenotyping of Heart Failure patients with Dutch discharge letters
MOX - Modelling and Scientific Computing Lab, Department of Mathematics, Politecnico di Milano, Milano, Italy（数学系模拟能力与科学计算实验室，米兰理工大学，意大利）；Department of Cardiology, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands（心脏病学部，阿姆斯特丹大学医学中心，阿姆斯特丹，荷兰）；Department of Medical Informatics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands（医学信息学部，阿姆斯特丹大学医学中心，阿姆斯特丹，荷兰）；Amsterdam Public Health, Digital Health, Personalized Medicine, Amsterdam, The Netherlands（阿姆斯特丹公共卫生，数字健康，个性化医学，荷兰阿姆斯特丹）；Amsterdam Cardiovascular Sciences, Atherosclerosis and Aortic Disease, Cardiomyopathy and Arrhythmia, Amsterdam, The Netherlands（阿姆斯特丹心血管科学，动脉粥样硬化和主动脉疾病，心肌病和心律失常，荷兰阿姆斯特丹）；Amsterdam Reproduction & Development, Amsterdam, The Netherlands（阿姆斯特丹生殖与发育，荷兰阿姆斯特丹）；Department of Epidemiology and Data Science, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands（流行病学与数据科学部，阿姆斯特丹大学医学中心，阿姆斯特丹，荷兰）；Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden（临床科学与教育部，索德斯医院，卡罗林斯卡研究所，斯德哥尔摩，瑞典）
A Self-Supervised Learning-based Approach to Clustering Multivariate Time-Series Data with Missing Values (SLAC-Time): An Application to TBI Phenotyping
University of Arizona（亚利桑那大学）；University of Cincinnati（辛辛那提大学）；Virginia Tech（弗吉尼亚理工大学）
DANIEL: A Distributed and Scalable Approach for Global Representation Learning with EHR Applications
Harvard University（哈佛大学）；University of Chicago（芝加哥大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Pittsburgh（匹兹堡大学）；Brigham and Women’s Hospital（哈佛医学院布莱尔妇女医院）
本研究提出一种基于 Ising 模型的分布式框架 DANIEL，通过双因子梯度下降优化非凸损失，在多机构 EHR 数据上实现了隐私保护且可扩展的全局表征学习，并在多项下游临床任务中表现卓越。
High-Throughput Phenotyping of Clinical Text Using Large Language Models
Dept. of Neurology \& Rehabilitation University of Illinois at Chicago Chicago, IL USA；Engineering Program Missouri State University Springfield, MO USA
Classifying Long Clinical Documents with Pre-trained Transformers
University of Arizona（亚利桑那大学）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；Loyola University Chicago（芝加哥洛约拉大学）
Contextual Phenotyping of Pediatric Sepsis Cohort Using Large Language Models
Department of Surgery, Duke university school of medicine（杜克大学医学学院外科部）；Department of Anesthesiology, Duke university school of medicine（杜克大学医学学院麻醉科）；Indian Institute of Technology, Goa（印度去邦理工学院）；Institute for Global Health, BC Children’s Hospital and BC Women’s Hospital + Health Centre（全球健康研究所，BC儿童医院和BC妇女医院及健康中心）；Department of Anesthesia, Pharmacology & Therapeutics, University of British Columbia（不列颠哥伦比亚大学麻醉学、药理学与治疗学系）；BC Children’s Hospital Research Institute, BC Children’s Hospital（BC儿童医院研究学院，BC儿童医院）；Department of Pediatrics, University of British Columbia（不列颠哥伦比亚大学儿科系）；Walimu, Kampala, Uganda（乌干达坎帕拉沃尔imu）；Department of Community Health, Mbarara University of Science and Technology（马巴拉拉大学科学与技术学院社区健康系）
A Corpus for Detecting High-Context Medical Conditions in Intensive Care Patient Notes Focusing on Frequently Readmitted Patients
Massachusetts Institute of Technology（麻省理工学院）；University of Massachusetts（马萨诸塞大学）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；Massachusetts General Hospital（麻省总医院）；Tufts University School of Medicine（塔夫茨大学医学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Washington University School of Medicine（华盛顿大学医学院）；Philips Research North America（飞利浦北美研究院）；Harvard University（哈佛大学）；Adobe Research（Adobe研究院）
An evaluation of GPT models for phenotype concept recognition
Rare Care Centre（罕见病护理中心）；Perth Children's Hospital（珀斯儿童医院）；Telethon Kids Institute（Telethon儿童研究所）；School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University（科廷大学电气工程、计算与数学科学学院）；SingHealth Duke-NUS Institute of Precision Medicine（新加坡保健服务集团杜克-新加坡国立大学精准医学研究所）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Western Australian Register of Developmental Anomalies（西澳大利亚发育异常登记处）；King Edward Memorial Hospital（爱德华国王纪念医院）；Faculty of Health and Medical Sciences, University of Western Australia（西澳大利亚大学健康与医学科学学院）；University of Colorado Anschutz Medical Campus（科罗拉多大学安舒茨医学校区）；The Jackson Laboratory for Genomic Medicine（杰克逊基因组医学实验室）；Institute for Systems Genomics, University of Connecticut（康涅狄格大学系统基因组学研究所）
