Rethinking PICO in the Machine Learning Era: ML-PICO

Xinran Liu, James Anstey, Ron Li, Chethan Sarabu, Reiri Sono, Atul J. Butte

Research output: Contribution to journalReview articlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)407-416
Number of pages10
JournalApplied Clinical Informatics
Volume12
Issue number2
DOIs
StatePublished - Mar 1 2021
Externally publishedYes

Keywords

  • artificial intelligence
  • electronic health record
  • machine learning

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications
  • Health Information Management

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