Rethinking PICO in the Machine Learning Era: ML-PICO
| Autor: | Ron C. Li, James E. Anstey, Reiri Sono, Xinran Liu, Chethan Sarabu, Atul J. Butte |
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| Rok vydání: | 2021 |
| Předmět: |
Outcome Assessment
media_common.quotation_subject Population Clinical Sciences MEDLINE Health Informatics Bioengineering 030204 cardiovascular system & hematology Machine learning computer.software_genre Machine Learning 03 medical and health sciences 0302 clinical medicine Health Information Management Health care Outcome Assessment Health Care Relevance (information retrieval) Quality (business) 030212 general & internal medicine education media_common education.field_of_study business.industry Diagnostic test electronic health record artificial intelligence Computer Science Applications Health Care Identification (information) Good Health and Well Being Artificial intelligence business computer Information Systems |
| Zdroj: | Appl Clin Inform Applied clinical informatics, vol 12, iss 2 |
| ISSN: | 1869-0327 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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