Machine Learning–Based Hospital Discharge Prediction for Patients With Cardiovascular Diseases: Development and Usability Study

作者：Imjin Ahn, Hansle Gwon, Hee Jun Kang, Yunha Kim, Yunha Kim, Hyeram Seo, Heejung Choi, Ha Na Cho, Minkyoung Kim, Tae Joon Jun, Young‐Hak Kim, Young‐Hak Kim · 发表于：JMIR Medical Informatics · 年份：2021 · DOI：10.2196/32662 · 被引用次数：30 · 研究领域：Machine Learning in Healthcare、Heart Failure Treatment and Management、Healthcare Operations and Scheduling Optimization

BACKGROUND: Effective resource management in hospitals can improve the quality of medical services by reducing labor-intensive burdens on staff, decreasing inpatient waiting time, and securing the optimal treatment time. The use of hospital processes requires effective bed management; a stay in the hospital that is longer than the optimal treatment time hinders bed management. Therefore, predicting a patient's hospitalization period may support the making of judicious decisions regarding bed management. OBJECTIVE: First, this study aims to develop a machine learning (ML)-based predictive model for predicting the discharge probability of inpatients with cardiovascular diseases (CVDs). Second, we aim to assess the outcome of the predictive model and explain the primary risk factors of inpatients for patient-specific care. Finally, we aim to evaluate whether our ML-based predictive model helps manage bed scheduling efficiently and detects long-term inpatients in advance to improve the use of hospital processes and enhance the quality of medical services. METHODS: We set up the cohort criteria and extracted the data from CardioNet, a manually curated database that specializes in CVDs. We processed the data to create a suitable data set by reindexing the date-index, integrating the present features with past features from the previous 3 years, and imputing missing values. Subsequently, we trained the ML-based predictive models and evaluated them to find an elaborate model. Finally...