From 2017 to 2018 the number of scientific publications found via PubMed search using the keyword "Machine Learning" increased by 46% (4,317 to 6,307). The results of studies involving machine learning, artificial intelligence (AI), and big data have captured the attention of healthcare practitioners, healthcare managers, and the public at a time when Western medicine grapples with unmitigated cost increases and public demands for accountability. The complexity involved in healthcare applications of machine learning and the size of the associated data sets has afforded many researchers an uncontested opportunity to satisfy these demands with relatively little oversight. In a recent Nature Medicine article, "The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care," Komorowski and his coauthors propose methods to train an artificial intelligence clinician to treat sepsis patients with vasopressors and IV fluids. In this post, we will closely examine the claims laid out in this paper. In particular, we will study the individual treatment profiles suggested by their AI Clinician to gain insight into how their AI Clinician intends to treat patients on an individual level.
Understanding the Artificial Intelligence Clinician and optimal treatment strategies for sepsis in intensive care
Imperial College London（伦敦帝国理工学院）；Harvard–MIT Division of Health Sciences & Technology（哈佛-麻省理工学院健康科学与技术部）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Philips Healthcare（飞利浦医疗）；University of Maryland, School of Pharmacy（马里兰大学药学院）；MRC London Institute of Medical Sciences（MRC伦敦医学科学研究所）；Data Science Institute（数据科学研究所）
Heart Rate Variability and Respiration Signal as Diagnostic Tools for Late Onset Sepsis in Neonatal Intensive Care Units
INSERM（法国国家健康与医学研究院）；Université de Rennes 1（雷恩第一大学）；CHU Rennes（雷恩大学医院）；Southeast University（东南大学）；Centre de Recherche en Information Biomédicale sino-français（中法生物医学信息研究中心）；University of California, San Francisco（加利福尼亚大学旧金山分校）
Synthetic Acute Hypotension and Sepsis Datasets Based on MIMIC-III and Published as Part of the Health Gym Project
University of New South Wales（新南威尔士大学）；Australian National University（澳大利亚国立大学）；The George Institute for Global Health（乔治全球健康研究院）；Imperial College London（伦敦帝国理工学院）
Learning Preference-Based Objectives from Clinical Narratives for Dynamic Sepsis Treatment
Institute of Data Science, National University of Singapore, Singapore（新加坡国立大学数据科学研究所）；National University Hospital, Singapore（新加坡国立大学医院）；Saw Swee Hock School of Public Health, National University of Singapore, Singapore（新加坡国立大学 Saw Swee Hock 公共卫生学院）
A federated learning framework with knowledge graph and temporal transformer for early sepsis prediction in multi-center ICUs
Chengdu Medical College（成都医学院）；Kunming Medical University（昆明医科大学）；National Yang Ming Chiao Tung University（国立阳明交通大学）；University of Southern California（南加州大学）；Yanshan University（燕山大学）
Transforming Tuberculosis Care: Optimizing Large Language Models For Enhanced Clinician-Patient Communication
School of Engineering and Technology, University of Washington Tacoma（华盛顿大学塔科马分校工程与技术学院）；Institute for Clinical Effectiveness and Health Policy（临床效果与健康政策研究所）；Centre for Research on Epidemiology and Public Health (CIESP), CONICET（CONICET流行病学与公共卫生研究中心）；School of Nursing, University of Washington Seattle（华盛顿大学西雅图分校护理学院）；School of Nursing and Healthcare Leadership, University of Washington Tacoma（华盛顿大学塔科马分校护理与医疗保健领导学院）
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）
Agentic AI for Scaling Diagnosis and Care in Neurodegenerative Disease
Goizueta Brain Health Institute, Emory University（埃默里大学戈伊苏埃塔大脑健康研究所）；University of California, San Francisco（加州大学旧金山分校）；Georgia Institute of Technology（佐治亚理工学院）；University of California, Berkeley（加州大学伯克利分校）
MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records
Stanford School of Medicine（斯坦福大学医学院）；Stanford School of Engineering（斯坦福大学工程学院）；Stanford University（斯坦福大学）；Stanford Health Care（斯坦福医疗保健）；Vanderbilt University School of Medicine（范德比尔特大学医学院）；Vanderbilt University Medical Center（范德比尔特大学医学中心）；Hospital Israelita Albert Einstein（以色列阿尔伯特·爱因斯坦医院）
Evaluation of General Large Language Models in Contextually Assessing Semantic Concepts Extracted from Adult Critical Care Electronic Health Record Notes
