Machine Learning and Statistics in Clinical Research Articles-Moving Past the False Dichotomy

Samuel G Finlayson; Andrew L Beam; Maarten van Smeden

doi:10.1001/jamapediatrics.2023.0034

Machine Learning and Statistics in Clinical Research Articles-Moving Past the False Dichotomy

JAMA Pediatr. 2023 May 1;177(5):448-450. doi: 10.1001/jamapediatrics.2023.0034.

Authors

Samuel G Finlayson^{1

2}, Andrew L Beam³, Maarten van Smeden⁴

Affiliations

¹ Department of Pediatrics, Seattle Children's Hospital, Seattle, Washington.
² Department of Genetics, University of Washington, Seattle.
³ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
⁴ Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.

PMID: 36939696
DOI: 10.1001/jamapediatrics.2023.0034

No abstract available

Plain language summary

This Viewpoint describes the false dichotomy between statistics and machine learning and suggests considerations in building and evaluating clinical prediction models.

MeSH terms

Humans
Machine Learning*
Statistics as Topic*

Grants and funding

K01 HL141771/HL/NHLBI NIH HHS/United States