Electronic health records and stratified psychiatry: bridge to precision treatment?
Journal
Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
ISSN: 1740-634X
Titre abrégé: Neuropsychopharmacology
Pays: England
ID NLM: 8904907
Informations de publication
Date de publication:
Jan 2024
Jan 2024
Historique:
received:
11
08
2023
accepted:
27
08
2023
revised:
24
08
2023
medline:
11
12
2023
pubmed:
5
9
2023
entrez:
4
9
2023
Statut:
ppublish
Résumé
The use of a stratified psychiatry approach that combines electronic health records (EHR) data with machine learning (ML) is one potentially fruitful path toward rapidly improving precision treatment in clinical practice. This strategy, however, requires confronting pervasive methodological flaws as well as deficiencies in transparency and reporting in the current conduct of ML-based studies for treatment prediction. EHR data shares many of the same data quality issues as other types of data used in ML prediction, plus some unique challenges. To fully leverage EHR data's power for patient stratification, increased attention to data quality and collection of patient-reported outcome data is needed.
Identifiants
pubmed: 37667021
doi: 10.1038/s41386-023-01724-y
pii: 10.1038/s41386-023-01724-y
pmc: PMC10700348
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
285-290Informations de copyright
© 2023. The Author(s).
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