Multiple organ failure (MOF) is a life-threatening condition. Due to its urgency and high mortality rate, early detection is critical for clinicians to provide appropriate treatment. In this paper, we perform quantitative analysis on early MOF prediction with comprehensive machine learning (ML) configurations, including data preprocessing (missing value treatment, label balancing, feature scaling), feature selection, classifier choice, and hyperparameter tuning. Results show that classifier choice impacts both the performance improvement and variation most among all the configurations. In general, complex classifiers including ensemble methods can provide better performance than simple classifiers. However, blindly pursuing complex classifiers is unwise as it also brings the risk of greater performance variation.
Rare Event Early Detection: A Dataset of Sepsis Onset for Critically Ill Trauma Patients
University of Washington Tacoma WA USA（华盛顿大学塔科马分校）；Department of Statistics University of Washington Seattle WA USA（华盛顿大学统计学系）；Department of Surgery University of Washington Seattle WA USA（华盛顿大学外科系）
Robust Meta-Model for Predicting the Need for Blood Transfusion in Non-traumatic ICU Patients
Emory University（埃默里大学）；Emory University School of Medicine（埃默里大学医学院）；The Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）；Johns Hopkins All Children’s Hospital（约翰斯·霍普金斯全儿童医院）
Universidade da Coruña（拉科鲁尼亚大学）；Instituto de Investigación Biomédica de A Coruña (INIBIC)（拉科鲁尼亚生物医学研究所）；Complexo Hospitalario Universitario de A Coruña (CHUAC)（拉科鲁尼亚大学医院综合体）；Health Research Institute of Santiago de Compostela (IDIS)（圣地亚哥-德孔波斯特拉健康研究所）
Predicting tacrolimus exposure in kidney transplanted patients using machine learning
SimulaMet；Oslo Metropolitan University（奥斯陆城市大学）；Norwegian University of Science and Technology（挪威科技大学）；Oslo University Hospital（奥斯陆大学医院）；University of Oslo（奥斯陆大学）；UiT The Arctic University of Norway（挪威北极大学）
Organ-Agents: Virtual Human Physiology Simulator via LLMs
Tianjin University（天津大学）；University of Trento（特伦托大学）；Tianjin Medical University General Hospital（天津医科大学总医院）；Chest Hospital, Tianjin University（天津大学胸科医院）；The Affiliated Suzhou Hospital of Nanjing Medical University（南京医科大学附属苏州医院）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）
Three-Dimensional Radiotherapy Dose Prediction on Head and Neck Cancer Patients with a Hierarchically Densely Connected U-net Deep Learning Architecture
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
Interpretable Machine Learning Model for Early Prediction of Mortality in Elderly Patients with Multiple Organ Dysfunction Syndrome (MODS): a Multicenter Retrospective Study and Cross Validation
