1067. Predicting next 7-day Discharges of Hospitalized COVID-19 Patients Using Ensemble Learning

Abstract

Abstract Background COVID-19 pandemic, especially during resurgences of cases in hard-hit areas, led to significant shortage of hospital beds. Such shortages may be alleviated through timely and effective forecasting of hospital discharges. The objective of this study is to predict next 7-day discharges of hospitalized COVID-19 patients using daily-based electronic health records (EHR) data. Methods Using EHR data of hospitalized COVID-19 patients from 03/2020-08/2021, we employed ensemble learning to predict next 7-day discharges of individual patients. We used both baseline and daily inpatient features for model training, validation, and test. Baseline features include demographic and clinical characteristics, and comorbidities. The daily inpatient features were vital signs, laboratory tests, medications administered, acute physiological scores, use of ventilator, and use of intensive care unit. 1832 hospitalized patients were identified (12,397 hospital days). Samples were randomly split at patient level (7:2:1) into training set (N=1,283 patients with 8,704 hospital days), validation set (N=366 patients with 2,524 days), and test/holdout set (patient N=183, and 1,169 days). Prediction models were trained on the training set and the validation set. We conducted the model training separately on the samples of admission day and the samples of days after admission day. The predictions were based on the ensemble learning from decision tree, XGBoost, logistic regression, and multilayer perceptron, long short-term memory (LSTM), bi-directional LSTM, and convolutional neural network. The combination of ensemble learning on the test/holdout set was used for final next 7-day predictions based on ‘hard’ voting (by majority). Where there was a tie, we used ‘soft’ voting (sum of probabilities) to break the tie. Figure 1.Data Processing PipelineFigure 2.Ensemble Model Architecture Results The overall average hospital length of stay was 8.7 (SD=10.5) days. The ensemble learning accuracies for admission-day samples and after-admission-day samples were 0.781 and 0.793, and the F1-scores for were 0.761 and 0.789, respectively. Conclusion EHR data of hospitalized COVID-19 patients can be used to predict next 7-day hospital discharges. Additional inpatient features and more advanced machine learning techniques are needed for prediction accuracy improvement. Disclosures All Authors: No reported disclosures.