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

doi:10.3389/fped.2022.864755

. 2022 May 10:10:864755.

doi: 10.3389/fped.2022.864755. eCollection 2022.

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

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.

PMID: 35620143
PMCID: PMC9127438
DOI: 10.3389/fped.2022.864755

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

Daniel Ehrmann et al. Front Pediatr. 2022.

. 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.

PMID: 35620143
PMCID: PMC9127438
DOI: 10.3389/fped.2022.864755

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.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

**Figure 1**
One potential roadmap for leveraging existing curricular implementation resources common to many PCCM training programs. Resources are divided by the ML in PCCM curriculum objectives.

See this image and copyright information in PMC

Cited by

Machine Vision and Image Analysis in Anesthesia: Narrative Review and Future Prospects.
Lonsdale H, Gray GM, Ahumada LM, Matava CT. Lonsdale H, et al. Anesth Analg. 2023 Oct 1;137(4):830-840. doi: 10.1213/ANE.0000000000006679. Epub 2023 Sep 5. Anesth Analg. 2023. PMID: 37712476 Free PMC article. Review.
Teaching old tools new tricks-preparing emergency medicine for the impact of machine learning-based risk prediction models.
Harish V, Grewal K, Mamdani M, Thiruganasambandamoorthy V. Harish V, et al. CJEM. 2023 May;25(5):365-369. doi: 10.1007/s43678-023-00480-8. Epub 2023 Mar 18. CJEM. 2023. PMID: 36933121 Free PMC article. No abstract available.

References

1. Patel VL, Kaufman DR, Arocha JF. Emerging paradigms of cognition in medical decision-making. J Biomed Inform. (2002) 35:52–75. 10.1016/S1532-0464(02)00009-6 - DOI - PubMed
1. Nemeth C, Blomberg J, Argenta C, Serio-Melvin ML, Salinas J, Pamplin J. Revealing ICU cognitive work through naturalistic decision-making methods. J Cogn Eng Decis Making. (2016) 10:350–68. 10.1177/1555343416664845 - DOI
1. Komorowski M. Artificial intelligence in intensive care: are we there yet? Intensive Care Med. (2019) 45:1298–300. 10.1007/s00134-019-05662-6 - DOI - PubMed
1. Gutierrez G. Artificial intelligence in the intensive care unit. Crit Care. (2020) 24:101. 10.1186/s13054-020-2785-y - DOI - PMC - PubMed
1. Nassar AP Jr, Caruso P. ICU physicians are unable to accurately predict length of stay at admission: a prospective study. Int J Qual Health Care. (2016) 28:99–103. 10.1093/intqhc/mzv112 - DOI - PubMed

LinkOut - more resources

Full Text Sources
Miscellaneous
- NCI CPTAC Assay Portal

[1] Patel VL, Kaufman DR, Arocha JF. Emerging paradigms of cognition in medical decision-making. J Biomed Inform. (2002) 35:52–75. 10.1016/S1532-0464(02)00009-6 - DOI - PubMed

[2] Patel VL, Kaufman DR, Arocha JF. Emerging paradigms of cognition in medical decision-making. J Biomed Inform. (2002) 35:52–75. 10.1016/S1532-0464(02)00009-6 - DOI - PubMed

[3] Nemeth C, Blomberg J, Argenta C, Serio-Melvin ML, Salinas J, Pamplin J. Revealing ICU cognitive work through naturalistic decision-making methods. J Cogn Eng Decis Making. (2016) 10:350–68. 10.1177/1555343416664845 - DOI

[4] Nemeth C, Blomberg J, Argenta C, Serio-Melvin ML, Salinas J, Pamplin J. Revealing ICU cognitive work through naturalistic decision-making methods. J Cogn Eng Decis Making. (2016) 10:350–68. 10.1177/1555343416664845 - DOI

[5] Komorowski M. Artificial intelligence in intensive care: are we there yet? Intensive Care Med. (2019) 45:1298–300. 10.1007/s00134-019-05662-6 - DOI - PubMed

[6] Komorowski M. Artificial intelligence in intensive care: are we there yet? Intensive Care Med. (2019) 45:1298–300. 10.1007/s00134-019-05662-6 - DOI - PubMed

[7] Gutierrez G. Artificial intelligence in the intensive care unit. Crit Care. (2020) 24:101. 10.1186/s13054-020-2785-y - DOI - PMC - PubMed

[8] Gutierrez G. Artificial intelligence in the intensive care unit. Crit Care. (2020) 24:101. 10.1186/s13054-020-2785-y - DOI - PMC - PubMed

[9] Nassar AP Jr, Caruso P. ICU physicians are unable to accurately predict length of stay at admission: a prospective study. Int J Qual Health Care. (2016) 28:99–103. 10.1093/intqhc/mzv112 - DOI - PubMed

[10] Nassar AP Jr, Caruso P. ICU physicians are unable to accurately predict length of stay at admission: a prospective study. Int J Qual Health Care. (2016) 28:99–103. 10.1093/intqhc/mzv112 - DOI - PubMed

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

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

Affiliations

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

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

Similar articles

Cited by

References

LinkOut - more resources

Full Text Sources

Miscellaneous