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Elicitation of domain knowledge for a machine learning model for paediatric critical illness in South Africa

Pienaar, Michael A; Sempa, Joseph B; Luwes, Nicolaas; George, Elizabeth C; Brown, Stephen C; (2023) Elicitation of domain knowledge for a machine learning model for paediatric critical illness in South Africa. Frontiers in Pediatrics , 11 , Article 1005579. 10.3389/fped.2023.1005579. Green open access

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Abstract

OBJECTIVES: Delays in identification, resuscitation and referral have been identified as a preventable cause of avoidable severity of illness and mortality in South African children. To address this problem, a machine learning model to predict a compound outcome of death prior to discharge from hospital and/or admission to the PICU was developed. A key aspect of developing machine learning models is the integration of human knowledge in their development. The objective of this study is to describe how this domain knowledge was elicited, including the use of a documented literature search and Delphi procedure. DESIGN: A prospective mixed methodology development study was conducted that included qualitative aspects in the elicitation of domain knowledge, together with descriptive and analytical quantitative and machine learning methodologies. SETTING: A single centre tertiary hospital providing acute paediatric services. PARTICIPANTS: Three paediatric intensivists, six specialist paediatricians and three specialist anaesthesiologists. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The literature search identified 154 full-text articles reporting risk factors for mortality in hospitalised children. These factors were most commonly features of specific organ dysfunction. 89 of these publications studied children in lower- and middle-income countries. The Delphi procedure included 12 expert participants and was conducted over 3 rounds. Respondents identified a need to achieve a compromise between model performance, comprehensiveness and veracity and practicality of use. Participants achieved consensus on a range of clinical features associated with severe illness in children. No special investigations were considered for inclusion in the model except point-of-care capillary blood glucose testing. The results were integrated by the researcher and a final list of features was compiled. CONCLUSION: The elicitation of domain knowledge is important in effective machine learning applications. The documentation of this process enhances rigour in such models and should be reported in publications. A documented literature search, Delphi procedure and the integration of the domain knowledge of the researchers contributed to problem specification and selection of features prior to feature engineering, pre-processing and model development.

Type: Article
Title: Elicitation of domain knowledge for a machine learning model for paediatric critical illness in South Africa
Location: Switzerland
Open access status: An open access version is available from UCL Discovery
DOI: 10.3389/fped.2023.1005579
Publisher version: https://doi.org/10.3389/fped.2023.1005579
Language: English
Additional information: © 2023 Pienaar, Sempa, Luwes, George and Brown. 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.
Keywords: machine learning, critical care, children, domain knowledge, triage, severity of illness
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 > Inst of Clinical Trials and Methodology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology > MRC Clinical Trials Unit at UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10167048
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