Li Luo1, Jialing Li1, Chuang Liu2, Wenwu Shen3. 1. Business School, Sichuan University, Chengdu, China. 2. Logistics Engineering School, Chengdu Vocational & Technical College of Industry, Chengdu, China. 3. Outpatient Department, West China Hospital of Sichuan University, Chengdu, China.
Abstract
BACKGROUND: Large tertiary hospitals usually face long waiting lines; patients who want to receive hospitalization need to be screened in advance. The patient admission screening process involves a health-care professional ranking patients by analyzing registration information. OBJECTIVE: The purpose of this study was to develop a machine-learning approach to screening, using historical data and the experience of health-care professionals to develop a set of screening rules to help health-care professionals prioritize patient needs automatically. METHODS: We used five machine-learning methods to sequence and predict elective patients: logistic regression (LR), random forest (RF), gradient-boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and an ensemble model of the four models. RESULTS: The results indicate that all of the five models showed a good prioritization performance with high predictive values. In particular, XGBoost had the best predictive performance compared with others in terms of the area under the receiver operating characteristic curve (AUC), with the AUC values of LR, RF, GBDT, XGBoost, and the ensemble model being 0.881, 0.816, 0.820, 0.901, and 0.897, respectively. CONCLUSION: The results reported here indicate that machine-learning techniques can be valuable for automating the screening process. Our model can assist health-care professionals in automatically evaluating less complex cases by identifying important factors affecting patient admission.
BACKGROUND: Large tertiary hospitals usually face long waiting lines; patients who want to receive hospitalization need to be screened in advance. The patient admission screening process involves a health-care professional ranking patients by analyzing registration information. OBJECTIVE: The purpose of this study was to develop a machine-learning approach to screening, using historical data and the experience of health-care professionals to develop a set of screening rules to help health-care professionals prioritize patient needs automatically. METHODS: We used five machine-learning methods to sequence and predict elective patients: logistic regression (LR), random forest (RF), gradient-boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and an ensemble model of the four models. RESULTS: The results indicate that all of the five models showed a good prioritization performance with high predictive values. In particular, XGBoost had the best predictive performance compared with others in terms of the area under the receiver operating characteristic curve (AUC), with the AUC values of LR, RF, GBDT, XGBoost, and the ensemble model being 0.881, 0.816, 0.820, 0.901, and 0.897, respectively. CONCLUSION: The results reported here indicate that machine-learning techniques can be valuable for automating the screening process. Our model can assist health-care professionals in automatically evaluating less complex cases by identifying important factors affecting patient admission.
Authors: Kathrin Seibert; Dominik Domhoff; Dominik Bruch; Matthias Schulte-Althoff; Daniel Fürstenau; Felix Biessmann; Karin Wolf-Ostermann Journal: J Med Internet Res Date: 2021-11-29 Impact factor: 5.428