Appl Clin Inform 2021; 12(02): 407-416
DOI: 10.1055/s-0041-1729752
Review Article

Rethinking PICO in the Machine Learning Era: ML-PICO

Xinran Liu
1  Division of Hospital Medicine, University of California, San Francisco, San Francisco, California, United States
2  University of California, San Francisco, San Francisco, California, United States
,
James Anstey
1  Division of Hospital Medicine, University of California, San Francisco, San Francisco, California, United States
,
Ron Li
3  Division of Hospital Medicine, Stanford University, Stanford, California, United States
,
Chethan Sarabu
4  doc.ai, Palo Alto, California, United States
5  Department of Pediatrics, Stanford University, Stanford, California, United States
,
Reiri Sono
2  University of California, San Francisco, San Francisco, California, United States
,
Atul J. Butte
6  Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California, United States
› Author Affiliations

Abstract

Background Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers.

Objective We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care.

Conclusion The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers.

Protection of Human and Animal Subjects

Human and/or animal subjects were not included in this project.




Publication History

Received: 17 November 2020

Accepted: 24 March 2021

Publication Date:
19 May 2021 (online)

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