首页|Reports Outline Machine Learning Study Findings from Taipei Veterans General Hospital (Machine-learning models are superior to severity scoring systems for the prediction of the mortality of critically ill patients in a tertiary medical center)
Reports Outline Machine Learning Study Findings from Taipei Veterans General Hospital (Machine-learning models are superior to severity scoring systems for the prediction of the mortality of critically ill patients in a tertiary medical center)
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Investigators publish new report on artificial intelligence. According to news reporting out of the Taipei Veterans General Hospital by NewsRx editors, research stated, "Intensive care unit (ICU) mortality prediction helps to guide therapeutic decision making for critically ill patients. Several scoring systems based on statistical techniques have been developed for this purpose." The news editors obtained a quote from the research from Taipei Veterans General Hospital: "In this study, we developed a machine-learning model to predict patient mortality in the very early stage of ICU admission. This study was performed with data from all patients admitted to the ICUs of a tertiary medical center in Taiwan from 2009 to 2018. The patients' comorbidities, co-medications, vital signs, and laboratory data on the day of ICU admission were obtained from electronic medical records. We constructed random forest (RF) and extreme gradient boosting (XGBoost) models to predict ICU mortality, and compared their performance with that of traditional scoring systems. Data from 12,377 patients was allocated to training (n = 9901) and testing (n = 2476) datasets. The median patient age was 70.0 years; 9210 (74.41%) patients were under mechanical ventilation in the ICU. The areas under receiver operating characteristic curves for the RF and XGBoost models (0.876 and 0.880, respectively) were larger than those for the Acute Physiology and Chronic Health Evaluation Ⅱ score (0.738), Sequential Organ Failure Assessment score (0.747), and Simplified Acute Physiology Score Ⅱ (0.743). The fraction of inspired oxygen on ICU admission was the most important predictive feature across all models."
Taipei Veterans General HospitalCyborgsEmerging TechnologiesHospitalsMachine Learning
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