Early Detection of Septic Shock Onset Using Interpretable Machine Learners

作者：Debdipto Misra, Venkatesh Avula, Donna M. Wolk, Hosam A. Farag, Jiang Li, Yatin Mehta, Ranjeet Kaur Sandhu, Bipin Karunakaran, Shravan Kethireddy, Ramin Zand, Vida Abedi · 发表于：Journal of Clinical Medicine · 年份：2021 · DOI：10.3390/jcm10020301 · 被引用次数：51 · 研究领域：Sepsis Diagnosis and Treatment、Machine Learning in Healthcare、Artificial Intelligence in Healthcare and Education

BACKGROUND: Developing a decision support system based on advances in machine learning is one area for strategic innovation in healthcare. Predicting a patient's progression to septic shock is an active field of translational research. The goal of this study was to develop a working model of a clinical decision support system for predicting septic shock in an acute care setting for up to 6 h from the time of admission in an integrated healthcare setting. METHOD: from admission. Eight different machine learning algorithms (Random Forest, XGBoost, C5.0, Decision Trees, Boosted Logistic Regression, Support Vector Machine, Logistic Regression, Regularized Logistic, and Bayes Generalized Linear Model) were used for model development. Two adaptive sampling strategies were used to address the class imbalance. Data from two sources (clinical and billing codes) were used to define the case definition (septic shock) using the Centers for Medicare & Medicaid Services (CMS) Sepsis criteria. The model assessment was performed using Area under Receiving Operator Characteristics (AUROC), sensitivity, and specificity. Model predictions for each feature window (1, 3 and 6 h from admission) were consolidated. RESULTS: Retrospective data from April 2005 to September 2018 were extracted from the EHR, Insurance Claims, Billing, and Laboratory Systems to create a dataset for septic shock detection. The clinical criteria and billing information were used to label patients into two classes-septic sh...