Ignorance Isn't Bliss: We Must Close the Machine Learning Knowledge Gap in Pediatric Critical Care

Daniel Ehrmann; Daniel Ehrmann; Vinyas Harish; Vinyas Harish; Vinyas Harish; Felipe Morgado; Felipe Morgado; Felipe Morgado; Laura Rosella; Laura Rosella; Alistair Johnson; Alistair Johnson; Briseida Mema; Mjaye Mazwi; Mjaye Mazwi

doi:10.3389/fped.2022.864755

Frontiers in Pediatrics (May 2022)

Ignorance Isn't Bliss: We Must Close the Machine Learning Knowledge Gap in Pediatric Critical Care

Daniel Ehrmann,
Daniel Ehrmann,
Vinyas Harish,
Vinyas Harish,
Vinyas Harish,
Felipe Morgado,
Felipe Morgado,
Felipe Morgado,
Laura Rosella,
Laura Rosella,
Alistair Johnson,
Alistair Johnson,
Briseida Mema,
Mjaye Mazwi,
Mjaye Mazwi

Affiliations

Daniel Ehrmann: Department of Critical Care Medicine, Hospital for Sick Children, Toronto, ON, Canada
Daniel Ehrmann: Temerty Centre for Artificial Intelligence Research and Education in Medicine, University of Toronto, Toronto, ON, Canada
Vinyas Harish: Temerty Centre for Artificial Intelligence Research and Education in Medicine, University of Toronto, Toronto, ON, Canada
Vinyas Harish: MD/PhD Program, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
Vinyas Harish: Institute for Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
Felipe Morgado: Temerty Centre for Artificial Intelligence Research and Education in Medicine, University of Toronto, Toronto, ON, Canada
Felipe Morgado: MD/PhD Program, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
Felipe Morgado: Department of Medical Biophysics, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
Laura Rosella: MD/PhD Program, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
Laura Rosella: Institute for Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
Alistair Johnson: MD/PhD Program, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
Alistair Johnson: Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada
Briseida Mema: Department of Critical Care Medicine, Hospital for Sick Children, Toronto, ON, Canada
Mjaye Mazwi: Department of Critical Care Medicine, Hospital for Sick Children, Toronto, ON, Canada
Mjaye Mazwi: MD/PhD Program, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada

DOI: https://doi.org/10.3389/fped.2022.864755
Journal volume & issue: Vol. 10

Abstract

Read online

Pediatric intensivists are bombarded with more patient data than ever before. Integration and interpretation of data from patient monitors and the electronic health record (EHR) can be cognitively expensive in a manner that results in delayed or suboptimal medical decision making and patient harm. Machine learning (ML) can be used to facilitate insights from healthcare data and has been successfully applied to pediatric critical care data with that intent. However, many pediatric critical care medicine (PCCM) trainees and clinicians lack an understanding of foundational ML principles. This presents a major problem for the field. We outline the reasons why in this perspective and provide a roadmap for competency-based ML education for PCCM trainees and other stakeholders.

Published in Frontiers in Pediatrics

ISSN: 2296-2360 (Online)
Publisher: Frontiers Media S.A.
Country of publisher: Switzerland
LCC subjects: Medicine: Pediatrics
Website: http://journal.frontiersin.org/journal/pediatrics

About the journal

Abstract

Keywords