Forecasting Hospital Readmissions with Machine Learning

Journal article

Michailidis, P., Dimitriadou, A., Papadimitriou, T. and Gogas, P. 2022. Forecasting Hospital Readmissions with Machine Learning. Healthcare. 10 (918), pp. 1-14.
AuthorsMichailidis, P., Dimitriadou, A., Papadimitriou, T. and Gogas, P.

Hospital readmissions are regarded as a compounding economic factor for healthcare systems. In fact, the readmission rate is used in many countries as an indicator of the quality of services provided by a health institution. The ability to forecast patients’ readmissions allows for timely intervention and better post-discharge strategies, preventing future life-threatening events, and reducing medical costs to either the patient or the healthcare system. In this paper, four machine learning models are used to forecast readmissions: support vector machines with a linear kernel, support vector machines with an RBF kernel, balanced random forests, and weighted random forests. The dataset consists of 11,172 actual records of hospitalizations obtained from the General Hospital of Komotini “Sismanogleio” with a total of 24 independent variables. Each record is composed of administrative, medical-clinical, and operational variables. The experimental results indicate that the balanced random forest model outperforms the competition, reaching a sensitivity of 0.70 and an AUC value of 0.78.

Keywordsmachine learning; forecasting; readmissions
Journal citation10 (918), pp. 1-14
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Output statusPublished
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Online25 May 2022
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Accepted21 May 2022
Deposited02 Sep 2022
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