Patient-Specific Readmission Prediction and Intervention for Health Care



Published Jun 4, 2023
Yan Zhang


Hospital readmission is often associated with unfavorable patient outcomes and a large cost of resources. Therefore, preventing avoidable re-hospitalizations is imperative. To target this problem, one important metric that researchers and practitioners strive to reduce is the 30-day hospital readmission rate. In this paper, we introduce a general decision support system that utilizes machine learning (ML) based patientspecific prediction to guide the suggestion of patient intervention program assignment, with the objective of minimizing the readmission cost for hospitals. This work has three major contributions. First, the proposed solution is highly scalable by using PySpark. Second, we outline solution architecture components including (1) data injection (both real-time sensor reading and data at rest), processing, and analysis, and (2) ML model building, evaluation, deployment and scoring. Third, we discuss how the ML prediction results can be taken into account in a decision support system by presenting a rich visualization.

Abstract 257 | PDF Downloads 275



machine learning, hospital readmission, decison support system

Bayati, M., Braverman, M., Gillam, M., Mack, K. M., Ruiz, G., Smith, M. S., & Horvitz, E. (2014). Data-driven decisions for reducing readmissions for heart failure: General methodology and case study. PloS ONE, 9(10), 1–9.
McIlvennan, C. K., Eapen, Z. J., & Allen, L. A. (2015). Hospital readmissions reduction program. Circulation,131(20), 1796–1803.
Mortazavi, B. J., Downing, N. S., Bucholz, E. M., Dharmarajan, K., Manhapra, A., Li, S.-X., . . . Krumholz, H. M. (2016). Analysis of machine learning techniques for heart failure readmissions. Circulation: Cardiovascular Quality and Outcomes, CIRCOUTCOMES–116.
Ramkumar, P. N., Chu, C. T., Harris, J. D., Athiviraham, A., Harrington, M. A., White, D. L., . . . Li, L. T. (2015). Causes and rates of unplanned readmissions after elective primary total joint arthroplasty: a systematic review and meta-analysis. American journal of orthopedics, 44(9), 397–405.
Rumsfeld, J. S., Joynt, K. E., & Maddox, T. M. (2016). Big data analytics to improve cardiovascular care: promise and challenges. Nature Reviews Cardiology, 13(6), 350.
Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018). Deep ehr: A survey of recent advances in deep learning techniques for electronic health record (ehr) analysis. IEEE journal of biomedical and health informatics, 22(5), 1589–1604.
Strack, B., DeShazo, J. P., Gennings, C., Olmo, J. L., Ventura, S., Cios, K. J., & Clore, J. N. (2014). Impact of hba1c measurement on hospital readmission rates: analysis of 70,000 clinical database patient records. BioMed research international, 2014.
Zhang, Y., Bleik, S., & Wahl, M. (2017). Patient-specific readmission prediction and intervention for health care.
Zheng, B., Zhang, J., Yoon, S. W., Lam, S. S., Khasawneh, M., & Poranki, S. (2015). Predictive modeling of hospital readmissions using metaheuristics and data mining. Expert Systems with Applications, 42(20), 7110–7120.
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