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

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

doi:10.3389/fped.2022.864755

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

Front Pediatr. 2022 May 10:10:864755. doi: 10.3389/fped.2022.864755. eCollection 2022.

Authors

Daniel Ehrmann^{1

2}, Vinyas Harish^{2

3

4}, Felipe Morgado^{2

3

5}, Laura Rosella^{3

4}, Alistair Johnson^{3

6}, Briseida Mema¹, Mjaye Mazwi^{1

3}

Affiliations

¹ Department of Critical Care Medicine, Hospital for Sick Children, Toronto, ON, Canada.
² Temerty Centre for Artificial Intelligence Research and Education in Medicine, University of Toronto, Toronto, ON, Canada.
³ MD/PhD Program, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
⁴ Institute for Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
⁵ Department of Medical Biophysics, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
⁶ Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada.

Abstract

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.

Keywords: artificial intelligence; learning curricula; machine learning; medical education; pediatric critical care medicine.