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

2023/03/20 by Samuel G. Finlayson, Andrew L. Beam, Maarten van Smeden · 1 voice · 3 citations
Medicine · Decision Sciences · Computer Science · #Artificial Intelligence in Healthcare and Education #Meta-analysis and systematic reviews #Machine Learning in Healthcare

paper · doi:10.1001/jamapediatrics.2023.0034

Abstract

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

Cited by

• Updating methods for artificial intelligence–based clinical prediction models: a scoping review
• TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
• PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods

Discussions

• 4) Machine learning vs statistics Attempts to categorise analytical approaches into mutually exclusive groups of "machine learning", and "statistics" is often a waste of time and a source of false d [bsky, 41 points, 1 comments]

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