Abstract
Hospital readmissions pose additional costs and discomfort for the patient and their occurrences are indicative of deficient health service quality, hence efforts are generally made by medical professionals in order to prevent them. These endeavors are especially critical in the case of chronic conditions, such as diabetes. Recent developments in machine learning have been successful at predicting readmissions from the medical history of the diabetic patient. However, these approaches rely on a large number of clinical variables thereby requiring deep learning techniques. This article presents the application of simpler machine learning models achieving superior prediction performance while making computations more tractable.
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Notes
- 1.
UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/).
- 2.
Diabetes 130-US hospitals for years 1999–2008 Data Set (https://bit.ly/2kqU73b).
- 3.
ICD-9-CM Chapters (https://icd.codes/icd9cm).
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Ramírez, J.C., Herrera, D. (2019). Prediction of Diabetic Patient Readmission Using Machine Learning. In: Orjuela-Cañón, A., Figueroa-García, J., Arias-Londoño, J. (eds) Applications of Computational Intelligence. ColCACI 2019. Communications in Computer and Information Science, vol 1096. Springer, Cham. https://doi.org/10.1007/978-3-030-36211-9_7
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