時間：7月3日（周三）下午18:00-21:00
地點：主樓418
報告人：美國東卡羅來納大學梁會剛教授
報告內容簡介：
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.
報告人簡介：
梁會剛，美國東卡羅來納大學(East Carolina University)商學院管理信息系統系終身教授，醫療管理系統研究中心主任，2012年受聘為商學院唯一的杰出教授(Endowed Chair)。目前擔任國際期刊UTD24期刊Information Systems Research副主編，FT50期刊JAIS高級主編，Information & Management的副主編（Associate Editor）。在MIS Quarterly、Information System Research、Journal of Management Information System、Journal of AIS, Decision Support Systems等國際頂尖信息系統學術期刊發表論文30余篇。其著作被廣泛引用，據Google Scholar統計，引用次數已超過8000余次，單篇文章最高引用達到2459次。
（承辦：管理工程系、科研與學術交流中心）
