Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long short-term memory

Unplanned hospital readmission is an indicator of patients' exposure to risk and an avoidable waste of medical resources. To address the unplanned readmission issue, in 2010, the Affordable Care Act (ACA) created the Hospital Readmissions Reduction Program to penalize the hospitals whose 30-day readmission rates are higher than expected [1]. According to data released by the Centers for Medicare & Medicaid Services (CMS), since the program began on Oct. 1, 2012, hospitals have experienced nearly $2.5 billion of penalties assessed on hospitals for readmissions, including an estimated $564 million in fiscal year 2018, $144 million more than in 2016 [2]. In addition to hospital readmission, intensive care unit (ICU) readmission brings further financial risk, along with morbidity and mortality risks [3,4]. Premature ICU discharge may potentially expose patients to the risks of unsuitable treatment, which further leads to an avoidable mortality [5].