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Volume 22
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10.3390/ijerph22030345
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Article

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

by
Umit Tokac
1,
Jennifer Chipps
2,*,
Petra Brysiewicz
3,
John Bruce
4 and
Damian Clarke
4,5
1
College of Nursing, University of Missouri-St. Louis, St. Louis, MO 63121, USA
2
School of Nursing, Faculty of Community Health Sciences, University of Western Cape, Cape Town 7530, South Africa
3
School of Nursing and Public Health, University of KwaZulu-Natal, Durban 4041, South Africa
4
School of Clinical Medicine, University of KwaZulu-Natal, Durban 4041, South Africa
5
School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg 2017, South Africa
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2025, 22(3), 345; https://doi.org/10.3390/ijerph22030345
Submission received: 25 November 2024 / Revised: 21 February 2025 / Accepted: 23 February 2025 / Published: 26 February 2025
(This article belongs to the Special Issue Digital Health in South Africa)
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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 South Africa; machine learning; unplanned readmissions; trauma; surgery

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MDPI and ACS Style

Tokac, U.; Chipps, J.; Brysiewicz, P.; Bruce, J.; Clarke, D. Using Machine Learning to Improve Readmission Risk in Surgical Patients in South Africa. Int. J. Environ. Res. Public Health 2025, 22, 345. https://doi.org/10.3390/ijerph22030345

AMA Style

Tokac U, Chipps J, Brysiewicz P, Bruce J, Clarke D. Using Machine Learning to Improve Readmission Risk in Surgical Patients in South Africa. International Journal of Environmental Research and Public Health. 2025; 22(3):345. https://doi.org/10.3390/ijerph22030345

Chicago/Turabian Style

Tokac, Umit, Jennifer Chipps, Petra Brysiewicz, John Bruce, and Damian Clarke. 2025. "Using Machine Learning to Improve Readmission Risk in Surgical Patients in South Africa" International Journal of Environmental Research and Public Health 22, no. 3: 345. https://doi.org/10.3390/ijerph22030345

APA Style

Tokac, U., Chipps, J., Brysiewicz, P., Bruce, J., & Clarke, D. (2025). Using Machine Learning to Improve Readmission Risk in Surgical Patients in South Africa. International Journal of Environmental Research and Public Health, 22(3), 345. https://doi.org/10.3390/ijerph22030345

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