An artificial neural network model for pediatric mortality prediction in two tertiary pediatric intensive care units in South Africa. A development study

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dc.contributor.authorPienaar, Michael A.
dc.contributor.authorSempa, Joseph B.
dc.contributor.authorLuwes, Nicolaas
dc.contributor.authorSolomon, Lincoln J.
dc.date.accessioned2022-04-25T13:39:01Z
dc.date.available2022-04-25T13:39:01Z
dc.date.issued2022
dc.description.abstractObjectives: The performance of mortality prediction models remain a challenge in lower- and middle-income countries. We developed an artificial neural network (ANN) model for the prediction of mortality in two tertiary pediatric intensive care units (PICUs) in South Africa using free to download and use software and commercially available computers. These models were compared to a logistic regression model and a recalibrated version of the Pediatric Index of Mortality 3. Design: This study used data from a retrospective cohort study to develop an artificial neural model and logistic regression model for mortality prediction. The outcome evaluated was death in PICU. Setting: Two tertiary PICUs in South Africa. Patients: 2,089 patients up to the age of 13 completed years were included in the study. Interventions: None. Measurements and Main Results: The AUROC was higher for the ANN (0.89) than for the logistic regression model (LR) (0.87) and the recalibrated PIM3 model (0.86). The precision recall curve however favors the ANN over logistic regression and recalibrated PIM3 (AUPRC = 0.6 vs. 0.53 and 0.58, respectively. The slope of the calibration curve was 1.12 for the ANN model (intercept 0.01), 1.09 for the logistic regression model (intercept 0.05) and 1.02 (intercept 0.01) for the recalibrated version of PIM3. The calibration curve was however closer to the diagonal for the ANN model. Conclusions: Artificial neural network models are a feasible method for mortality prediction in lower- and middle-income countries but significant challenges exist. There is a need to conduct research directed toward the acquisition of large, complex data sets, the integration of documented clinical care into clinical research and the promotion of the development of electronic health record systems in lower and middle income settings.en_ZA
dc.description.versionPublisher's versionen_ZA
dc.identifierhttps://doi.org/10.3389/fped.2022.797080
dc.identifier.citationPienaar, M.A., Sempa, J.B., Luwes, N., & Solomon, L.J. (2022). An artificial neural network model for pediatric mortality prediction in two tertiary pediatric intensive care units in South Africa. A development study. Frontiers in Pediatrics, 10, 797080. https://doi.org/10.3389/fped.2022.797080en_ZA
dc.identifier.issn2296-2360
dc.identifier.urihttp://hdl.handle.net/11660/11580
dc.language.isoenen_ZA
dc.publisherFrontiersen_ZA
dc.rights.holderAuthor(s)en_ZA
dc.rights.licensehttp://creativecommons.org/licenses/by/4.0/
dc.subjectCritical careen_ZA
dc.subjectChildrenen_ZA
dc.subjectSeverity of illnessen_ZA
dc.subjectMachine learningen_ZA
dc.subjectArtificial neural networken_ZA
dc.titleAn artificial neural network model for pediatric mortality prediction in two tertiary pediatric intensive care units in South Africa. A development studyen_ZA
dc.typeArticleen_ZA
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