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 note: This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ 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