Abstract
For chemotherapy patients, unplanned emergency department (ED) visits and inpatient hospitalization (IP) stays are common and costly. Recent evidence suggests a deep learning algorithm (the Reverse Time Attention (RETAIN) model) can identify patients at elevated risk of adverse events to guide more efficient patient services. This study aims to investigate the intrinsic net benefit of a deep learning algorithm (i.e., RETAIN model) and the economic costs for a given threshold selected by the clinician.
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