Machine learning approaches for electronic health records phenotyping: a methodical review.
J Am Med Inform Assoc
; 30(2): 367-381, 2023 01 18.
Article
de En
| MEDLINE
| ID: mdl-36413056
ABSTRACT
OBJECTIVE:
Accurate and rapid phenotyping is a prerequisite to leveraging electronic health records for biomedical research. While early phenotyping relied on rule-based algorithms curated by experts, machine learning (ML) approaches have emerged as an alternative to improve scalability across phenotypes and healthcare settings. This study evaluates ML-based phenotyping with respect to (1) the data sources used, (2) the phenotypes considered, (3) the methods applied, and (4) the reporting and evaluation methods used. MATERIALS ANDMETHODS:
We searched PubMed and Web of Science for articles published between 2018 and 2022. After screening 850 articles, we recorded 37 variables on 100 studies.RESULTS:
Most studies utilized data from a single institution and included information in clinical notes. Although chronic conditions were most commonly considered, ML also enabled the characterization of nuanced phenotypes such as social determinants of health. Supervised deep learning was the most popular ML paradigm, while semi-supervised and weakly supervised learning were applied to expedite algorithm development and unsupervised learning to facilitate phenotype discovery. ML approaches did not uniformly outperform rule-based algorithms, but deep learning offered a marginal improvement over traditional ML for many conditions.DISCUSSION:
Despite the progress in ML-based phenotyping, most articles focused on binary phenotypes and few articles evaluated external validity or used multi-institution data. Study settings were infrequently reported and analytic code was rarely released.CONCLUSION:
Continued research in ML-based phenotyping is warranted, with emphasis on characterizing nuanced phenotypes, establishing reporting and evaluation standards, and developing methods to accommodate misclassified phenotypes due to algorithm errors in downstream applications.Mots clés
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Recherche biomédicale
/
Dossiers médicaux électroniques
Type d'étude:
Guideline
/
Prognostic_studies
Aspects:
Determinantes_sociais_saude
/
Equity_inequality
Langue:
En
Journal:
J Am Med Inform Assoc
Sujet du journal:
INFORMATICA MEDICA
Année:
2023
Type de document:
Article
Pays d'affiliation:
Canada