Teo, Kareen and Yong, Ching Wai and Chuah, Joon Huang and Murphy, Belinda Pingguan and Lai, Khin Wee (2021) Early detection of readmission risk for decision support based on clinical notes. Journal of Medical Imaging and Health Informatics, 11 (2). pp. 529-534. ISSN 2156-7018, DOI https://doi.org/10.1166/jmihi.2021.3304.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | Fundamental Research Grant Scheme (FRGS), Ministry of Education, Malaysia |
| Uncontrolled Keywords: | Readmission; Risk scoring; Electronic medical record; Machine learning; Natural language processing |
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 17 Feb 2022 01:44 |
| Last Modified: | 17 Feb 2022 01:46 |
| URI: | http://eprints.um.edu.my/id/eprint/26293 |
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