Machine learning based early mortality prediction in the emergency department. (November 2021) - Li, Cong; Zhang, Zhuo; Ren, Yazhou; Nie, Hu; Lei, Yuqing; Qiu, Hang; Xu, Zenglin; Pu, Xiaorong
Highlights: The LightGBM model with refined feature engineering demonstrated high discrimination among high-risk ED patients. The vital signs and laboratory tests were sufficiently informative to predict ED mortality. Machine learning models have significant potential as a clinical decision support tool in assisting physicians in their clinical routine. Abstract: Background: It is a great challenge for emergency physicians to early detect the patient's deterioration and prevent unexpected death through a large amount of clinical data, which requires sufficient experience and keen insight. Objective: To evaluate the performance of machine learning models in quantifying the severity of emergency department (ED) patients and identifying high-risk patients. Methods: Using routinely-available demographics, vital signs and laboratory tests extracted from electronic health records (EHRs), a framework based on machine learning and feature engineering was proposed for mortality prediction. Patients who had one complete record of vital signs and laboratory tests in ED were included. The following patients were excluded: pediatric patients aged < 18 years, pregnant woman, and patients died or were discharged or hospitalized within 12 h after admission. Based on 76 original features extracted, 9 machine learning models were adopted to validate our proposed framework. Their optimal hyper-parameters were fine-tuned using the grid search method. The prediction results were evaluated on performance metrics (i.e., accuracy, area under the curve (AUC), recall and precision) with repeated 5-fold cross-validation (CV). The time window from patient admission to the prediction was analyzed at 12 h, 24 h, 48 h, and entire stay. Results: We studied a total of 1114 ED patients with 71.54% (797/1114) survival and 28.46% (317/1114) death in the hospital. The results revealed a more complete time window leads to better prediction performance. Using the entire stay records, the LightGBM model with refined feature engineering demonstrated high discrimination and achieved 93.6% (±0.008) accuracy, 97.6% (±0.003) AUC, 97.1% (±0.008) recall, and 94.2% (±0.006) precision, even if no diagnostic information was utilized. Conclusions: This study quantifies the criticality of ED patients and appears to have significant potential as a clinical decision support tool in assisting physicians in their clinical routine. While the model requires validation before use elsewhere, the same methodology could be used to create a strong model for the new hospital.