AI-POWERED PREDICTIVE ANALYTICS FOR HOSPITAL READMISSION REDUCTION
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Keywords
Hospital, AI, Predictive Analytics, Readmission
Abstract
Being readmitted to the hospital within 30 days is still a problem for healthcare, adding to costs and affecting patients’ health results. In this paper, we discuss a full process using AI-based prediction to estimate the risk of hospital readmissions using information from electronic health records. Studying different machine learning techniques such as ensemble learning and deep learning, helps the study recognize patients at greatest risk and bring predictive results into doctors’ work routines. The results reveal that accuracy, recall and the organization of care have all improved. According to the research, AI can ensure hospitals plan care ahead of time using data which helps lower readmissions and improve the overall healthcare given.
References
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All articles published in JPTCP are licensed under Copyright Creative Commons Attribution-NonCommercial 4.0 International License.
Nikitha Edulakanti
Manager, Data and AI Solutions, Fresenius Medical Care