Predicting outcomes in older ED patients with influenza in real time using a big data-driven and machine learning approach to the hospital information system
Tian-Hoe Tan, Chien‐Chin Hsu, Chia‐Jung Chen, Shu‐Lien Hsu, Tzu-Lan Liu, Hung‐Jung Lin, Jhi‐Joung Wang, Chung‐Feng Liu, Chien‐Cheng Huang
Abstract
BACKGROUND: Predicting outcomes in older patients with influenza in the emergency department (ED) by machine learning (ML) has never been implemented. Therefore, we conducted this study to clarify the clinical utility of implementing ML. METHODS: We recruited 5508 older ED patients (≥65 years old) in three hospitals between 2009 and 2018. Patients were randomized into a 70%/30% split for model training and testing. Using 10 clinical variables from their electronic health records, a prediction model using the synthetic minority oversampling technique preprocessing algorithm was constructed to predict five outcomes. RESULTS: The best areas under the curves of predicting outcomes were: random forest model for hospitalization (0.840), pneumonia (0.765), and sepsis or septic shock (0.857), XGBoost for intensive care unit admission (0.902), and logistic regression for in-hospital mortality (0.889) in the testing data. The predictive model was further applied in the hospital information system to assist physicians' decisions in real time. CONCLUSIONS: ML is a promising way to assist physicians in predicting outcomes in older ED patients with influenza in real time. Evaluations of the effectiveness and impact are needed in the future.