ReviewNeuropsychopharmacology : official publication of the American College of Neuropsychopharmacology2024
Electronic health records and stratified psychiatry: bridge to precision treatment?
Review in Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Responsible artificial intelligence integration framework for psychiatric guidelines.The international journal of neuropsychopharmacology · 2026Guideline
- Big data and psychiatry: advances, constraints and future directions.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2026Article
- Predicting suicide attempts in a high-risk clinical cohort of adolescents using machine-learning.European child & adolescent psychiatry · 2026Article
- Correspondence between sleep patterns and anhedonia in adult male mice exposed to early-life stress.Translational psychiatry · 2025Article
- Large Language Models for Psychiatric Phenotype Extraction from Electronic Health Records.medRxiv : the preprint server for health sciences · 2025Article
- Treatment-resistant depression: role of genetic factors in the perspective of clinical stratification and treatment personalisation.Molecular psychiatry · 2025Review
- Predictive modeling of response to repetitive transcranial magnetic stimulation in treatment-resistant depression.Translational psychiatry · 2025Article
- Neuronal Synaptic Communication and Mitochondrial Energetics in Human Health and Disease.Advances in experimental medicine and biology · 2025Review
- How Real-World Data Can Facilitate the Development of Precision Medicine Treatment in Psychiatry.Biological psychiatry · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.