Yajiong Xue1, Huigang Liang2, John Norbury3, Rita Gillis4, Brenda Killingworth1. 1. Department of Management Information Systems, College of Business, East Carolina University, USA. 2. Department of Management Information Systems, College of Business, East Carolina University, USA; Center for Healthcare Management Systems, College of Business, East Carolina University, USA; Big Data and Analytics Research Cluster, East Carolina University, USA. Electronic address: liangh@ecu.edu. 3. Department of Physical Medicine & Rehabilitation, Brody School of Medicine, East Carolina University, USA. 4. Vidant Health, USA.
Abstract
INTRODUCTION: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.
INTRODUCTION: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.
Authors: Piotr Dworzynski; Martin Aasbrenn; Klaus Rostgaard; Mads Melbye; Thomas Alexander Gerds; Henrik Hjalgrim; Tune H Pers Journal: Sci Rep Date: 2020-02-04 Impact factor: 4.379
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