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Machine Learning Approaches to Determine Feature Importance for Predicting Infant Autopsy Outcome

Booth, J; Margetts, B; Bryant, W; Issitt, R; Hutchinson, C; Martin, N; Sebire, NJ; (2021) Machine Learning Approaches to Determine Feature Importance for Predicting Infant Autopsy Outcome. Pediatric and Developmental Pathology 10.1177/10935266211001644. (In press). Green open access

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Abstract

Introduction: Sudden unexpected death in infancy (SUDI) represents the commonest presentation of postneonatal death. We explored whether machine learning could be used to derive data driven insights for prediction of infant autopsy outcome. Methods: A paediatric autopsy database containing >7,000 cases, with >300 variables, was analysed by examination stage and autopsy outcome classified as ‘explained (medical cause of death identified)’ or ‘unexplained’. Decision tree, random forest, and gradient boosting models were iteratively trained and evaluated. Results: Data from 3,100 infant and young child (<2 years) autopsies were included. Naïve decision tree using external examination data had performance of 68% for predicting an explained death. Core data items were identified using model feature importance. The most effective model was XG Boost, with overall predictive performance of 80%, demonstrating age at death, and cardiovascular and respiratory histological findings as the most important variables associated with determining medical cause of death. Conclusion: This study demonstrates feasibility of using machine-learning to evaluate component importance of complex medical procedures (paediatric autopsy) and highlights value of collecting routine clinical data according to defined standards. This approach can be applied to a range of clinical and operational healthcare scenarios

Type: Article
Title: Machine Learning Approaches to Determine Feature Importance for Predicting Infant Autopsy Outcome
Open access status: An open access version is available from UCL Discovery
DOI: 10.1177/10935266211001644
Publisher version: http://dx.doi.org/10.1177/10935266211001644
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: infant death, autopsy, postmortem, features, machine learning
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Population, Policy and Practice Dept
URI: https://discovery.ucl.ac.uk/id/eprint/10128267
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