Evidence map›Paper›PMID 40238336›Full record

ArticleInternational journal of environmental research and public health2025

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

Umit Tokac, Jennifer Chipps, Petra Brysiewicz, John Bruce, Damian Clarke

Abstract read
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In one paragraph

Article in International journal of environmental research and public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Umit TokacCollege of Nursing, University of Missouri-St. Louis, St. Louis, MO 63121, USA.
Jennifer ChippsSchool of Nursing, Faculty of Community Health Sciences, University of Western Cape, Cape Town 7530, South Africa.ORCID 0000-0002-7895-4483
Petra BrysiewiczSchool of Nursing and Public Health, University of KwaZulu-Natal, Durban 4041, South Africa.ORCID 0000-0001-6258-1603
John BruceSchool of Clinical Medicine, University of KwaZulu-Natal, Durban 4041, South Africa.
Damian ClarkeSchool of Clinical Medicine, University of KwaZulu-Natal, Durban 4041, South Africa.

Funding

Medical Research CouncilUniversity Missouri South Africa Collaboration n/a
6 · The paper itself

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.

Indexed as

Machine LearningPatient ReadmissionSurgical Procedures, OperativeAdultAgedFemaleHumansMaleMiddle AgedNatural Language ProcessingRetrospective StudiesSouth Africamachine learningSouth Africasurgerytraumaunplanned readmissions

Identifiers

PMID40238336
PMCPMC11942159

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.