eprintid: 10161815
rev_number: 8
eprint_status: archive
userid: 699
dir: disk0/10/16/18/15
datestamp: 2022-12-15 13:35:32
lastmod: 2022-12-15 13:35:32
status_changed: 2022-12-15 13:35:32
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Pienaar, Michael A
creators_name: Sempa, Joseph B
creators_name: Luwes, Nicolaas
creators_name: George, Elizabeth C
creators_name: Brown, Stephen C
title: Development of artificial neural network models for paediatric critical illness in South Africa
ispublished: pub
divisions: UCL
divisions: B02
divisions: D65
divisions: J38
keywords: Science & Technology, Life Sciences & Biomedicine, Pediatrics, neural networks, machine learning, critical care, children, triage, severity of illness, MEAN ARTERIAL-PRESSURE, LOGISTIC-REGRESSION, TRIAGE, CHILDREN, PREDICTION, GUIDELINES, CURVE, AGE
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abstract: OBJECTIVES: Failures in identification, resuscitation and appropriate referral have been identified as significant contributors to avoidable severity of illness and mortality in South African children. In this study, artificial neural network models were developed to predict a composite outcome of death before discharge from hospital or admission to the PICU. These models were compared to logistic regression and XGBoost models developed on the same data in cross-validation. DESIGN: Prospective, analytical cohort study. SETTING: A single centre tertiary hospital in South Africa providing acute paediatric services. PATIENTS: Children, under the age of 13 years presenting to the Paediatric Referral Area for acute consultations. OUTCOMES: Predictive models for a composite outcome of death before discharge from hospital or admission to the PICU. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: 765 patients were included in the data set with 116 instances (15.2%) of the study outcome. Models were developed on three sets of features. Two derived from sequential floating feature selection (one inclusive, one parsimonious) and one from the Akaike information criterion to yield 9 models. All developed models demonstrated good discrimination on cross-validation with mean ROC AUCs greater than 0.8 and mean PRC AUCs greater than 0.53. ANN1, developed on the inclusive feature-et demonstrated the best discrimination with a ROC AUC of 0.84 and a PRC AUC of 0.64 Model calibration was variable, with most models demonstrating weak calibration. Decision curve analysis demonstrated that all models were superior to baseline strategies, with ANN1 demonstrating the highest net benefit. CONCLUSIONS: All models demonstrated satisfactory performance, with the best performing model in cross-validation being an ANN model. Given the good performance of less complex models, however, these models should also be considered, given their advantage in ease of implementation in practice. An internal validation study is now being conducted to further assess performance with a view to external validation.
date: 2022-11-15
date_type: published
publisher: FRONTIERS MEDIA SA
official_url: https://doi.org/10.3389/fped.2022.1008840
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1995176
doi: 10.3389/fped.2022.1008840
medium: Electronic-eCollection
lyricists_name: George, Elizabeth
lyricists_id: ERUSS31
actors_name: Flynn, Bernadette
actors_id: BFFLY94
actors_role: owner
funding_acknowledgements: [National Research Foundation of South Africa]; [Medical Research Council, UK]; MC_UU_00004/05 []
full_text_status: public
publication: Frontiers in Pediatrics
volume: 10
article_number: 1008840
pages: 13
event_location: Switzerland
citation:        Pienaar, Michael A;    Sempa, Joseph B;    Luwes, Nicolaas;    George, Elizabeth C;    Brown, Stephen C;      (2022)    Development of artificial neural network models for paediatric critical illness in South Africa.                   Frontiers in Pediatrics , 10     , Article 1008840.  10.3389/fped.2022.1008840 <https://doi.org/10.3389/fped.2022.1008840>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10161815/1/fped-10-1008840.pdf