Author:
Teo Kareen,Yong Ching Wai,Chuah Joon Huang,Murphy Belinda Pingguan,Lai Khin Wee
Abstract
Hospital readmission shortly after discharge is contributing to rising medical care costs. Attempts have been exerted to reduce readmission rates by predicting patients at high risk of this episode on the basis of unstructured clinical notes. Discharge summary as part of the clinical
prose is effective at modeling readmission risk. However, the predictive value of notes written upon discharge offers few opportunities to reduce the chance of readmission because the target patient might have already been discharged. This paper presents the use of early clinical notes in
building a machine learning model to predict readmission at 48 h immediately after a patient's admission. Extensive feature engineering, testing multiple algorithms, and algorithm tuning were performed to enhance model performance. A risk scoring framework that combines the data- and knowledge-driven
feature scores in risk computation was developed. The proposed predictive model showed better prognostic capability than the machine learning model alone in terms of the ability to detect readmission. In specific, the proposed algorithm showed improvements of 11%–28% in sensitivity and
1%–3% in the area-under-the-receiver operating characteristic curve.
Publisher
American Scientific Publishers
Subject
Health Informatics,Radiology, Nuclear Medicine and imaging
Cited by
5 articles.
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