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An Artificial Neural Network Model for Pediatric Mortality Prediction in Two Tertiary Pediatric Intensive Care Units in South Africa. A Development Study
OBJECTIVES: 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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8916561/ https://www.ncbi.nlm.nih.gov/pubmed/35281234 http://dx.doi.org/10.3389/fped.2022.797080 |
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author | Pienaar, Michael A. Sempa, Joseph B. Luwes, Nicolaas Solomon, Lincoln J. |
author_facet | Pienaar, Michael A. Sempa, Joseph B. Luwes, Nicolaas Solomon, Lincoln J. |
author_sort | Pienaar, Michael A. |
collection | PubMed |
description | OBJECTIVES: 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. |
format | Online Article Text |
id | pubmed-8916561 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89165612022-03-12 An Artificial Neural Network Model for Pediatric Mortality Prediction in Two Tertiary Pediatric Intensive Care Units in South Africa. A Development Study Pienaar, Michael A. Sempa, Joseph B. Luwes, Nicolaas Solomon, Lincoln J. Front Pediatr Pediatrics OBJECTIVES: 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. Frontiers Media S.A. 2022-02-25 /pmc/articles/PMC8916561/ /pubmed/35281234 http://dx.doi.org/10.3389/fped.2022.797080 Text en Copyright © 2022 Pienaar, Sempa, Luwes and Solomon. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Pediatrics Pienaar, Michael A. Sempa, Joseph B. Luwes, Nicolaas Solomon, Lincoln J. An Artificial Neural Network Model for Pediatric Mortality Prediction in Two Tertiary Pediatric Intensive Care Units in South Africa. A Development Study |
title | An Artificial Neural Network Model for Pediatric Mortality Prediction in Two Tertiary Pediatric Intensive Care Units in South Africa. A Development Study |
title_full | An Artificial Neural Network Model for Pediatric Mortality Prediction in Two Tertiary Pediatric Intensive Care Units in South Africa. A Development Study |
title_fullStr | An Artificial Neural Network Model for Pediatric Mortality Prediction in Two Tertiary Pediatric Intensive Care Units in South Africa. A Development Study |
title_full_unstemmed | An Artificial Neural Network Model for Pediatric Mortality Prediction in Two Tertiary Pediatric Intensive Care Units in South Africa. A Development Study |
title_short | An Artificial Neural Network Model for Pediatric Mortality Prediction in Two Tertiary Pediatric Intensive Care Units in South Africa. A Development Study |
title_sort | artificial neural network model for pediatric mortality prediction in two tertiary pediatric intensive care units in south africa. a development study |
topic | Pediatrics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8916561/ https://www.ncbi.nlm.nih.gov/pubmed/35281234 http://dx.doi.org/10.3389/fped.2022.797080 |
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