Using Machine Learning to Improve Readmission Risk in Surgical Patients in South Africa

Umit Tokac, Jennifer Chipps (), Petra Brysiewicz, John Bruce and Damian Clarke
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Umit Tokac: College of Nursing, University of Missouri-St. Louis, St. Louis, MO 63121, USA
Jennifer Chipps: School of Nursing, Faculty of Community Health Sciences, University of Western Cape, Cape Town 7530, South Africa
Petra Brysiewicz: School of Nursing and Public Health, University of KwaZulu-Natal, Durban 4041, South Africa
John Bruce: School of Clinical Medicine, University of KwaZulu-Natal, Durban 4041, South Africa
Damian Clarke: School of Clinical Medicine, University of KwaZulu-Natal, Durban 4041, South Africa

IJERPH, 2025, vol. 22, issue 3, 1-9

Abstract: Unplanned readmission within 30 days is a major challenge both globally and in South Africa. The aim of this study was to develop a machine learning model to predict unplanned surgical and trauma readmission to a public hospital in South Africa from unstructured text data. A retrospective cohort of records of patients was subjected to random forest analysis, using natural language processing and sentiment analysis to deal with data in free text in an electronic registry. Our findings were within the range of global studies, with reported AUC values between 0.54 and 0.92. For trauma unplanned readmissions, the discharge plan score was the most important predictor in the model, and for surgical unplanned readmissions, the problem score was the most important predictor in the model. The use of machine learning and natural language processing improved the accuracy of predicting readmissions.

Keywords: South Africa; machine learning; unplanned readmissions; trauma; surgery (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2025
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