Readmission from inpatient rehabilitation facilities to acute care hospitals is a serious problem. This study aims to develop a predictive model based on machine learning algorithms to identify patients at high risk of readmission. A retrospective dataset (2001–2017) including 16,902 patients admitted into a large inpatient rehabilitation facility in North Carolina was collected in 2017. Three types of machine learning models with different predictors were compared in 2018. The model with the highest c-statistic was selected as the best model and further tested by using five sets of training and validation data with different split time. The optimum threshold for classification was identified. The logistic regression model with only functional independence measures has the highest validation c-statistic at 0.852. Using this model to predict the recent 5 years acute care readmissions yielded high discriminative ability (c-statistics: 0.841–0.869). Larger training data yielded better performance on the test data. The default cutoff (0.5) resulted in high specificity (>0.997) but low sensitivity (<0.07). The optimum threshold helped to achieve a balance between sensitivity (0.754-0.867) and specificity (0.747-0.780). This study demonstrates that functional independence measures can be analyzed by using machine learning algorithms to predict acute care readmissions, thus improving the effectiveness of preventive medicine.
梁会刚，美国东卡罗来纳大学(East Carolina University)商学院管理信息系统系终身教授，医疗管理系统研究中心主任，2012年受聘为商学院唯一的杰出教授(Endowed Chair)。目前担任国际期刊UTD24期刊Information Systems Research副主编，FT50期刊JAIS高级主编，Information & Management的副主编（Associate Editor）。在MIS Quarterly、Information System Research、Journal of Management Information System、Journal of AIS, Decision Support Systems等国际顶尖信息系统学术期刊发表论文30余篇。其著作被广泛引用，据Google Scholar统计，引用次数已超过8000余次，单篇文章最高引用达到2459次。