TY - JOUR
T1 - Electronic health records and stratified psychiatry
T2 - bridge to precision treatment?
AU - Grzenda, Adrienne
AU - Widge, Alik S.
N1 - Publisher Copyright:
© 2023, The Author(s).
PY - 2024/1
Y1 - 2024/1
N2 - 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.
AB - 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.
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U2 - 10.1038/s41386-023-01724-y
DO - 10.1038/s41386-023-01724-y
M3 - Article
C2 - 37667021
AN - SCOPUS:85169687641
SN - 0893-133X
VL - 49
SP - 285
EP - 290
JO - Neuropsychopharmacology
JF - Neuropsychopharmacology
IS - 1
ER -