Hospital unexpected readmission using multi-model prediction
Conference: ICMLCA 2021 - 2nd International Conference on Machine Learning and Computer Application
12/17/2021 - 12/19/2021 at Shenyang, China
Proceedings: ICMLCA 2021
Pages: 5Language: englishTyp: PDF
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Authors:
Wu, Yuxuan (Computer Science Dept.Purdue University, West Lafayette, IN, USA)
Zhang, Wenxuan (Internet of Things, Hebei University of Technology, Tianjin, China)
Abstract:
Hospital unexpected readmission prediction can help hospitals prepare for unexpected patients, which avoids insufficient resource allocation. The prediction score can also be used by doctors to evaluate whether a particular patient is dischargeable or not. MIMIC-III is a big, publicly available database including information on over a thousand people and their medical conditions. The MIMIC-3 dataset consists of both structural (e.g. ICD, and demographic) and nonstructural (e.g. nursing notes) data. In this paper, we provided a multi-modal predictor of patient readmission by combining unstructured clinical text and structured data. The paper analytically shows the advantages of averaged multi-model in prediction of unplanned readmission.