ArticleEvidence-based mental health2017
Applying deep neural networks to unstructured text notes in electronic medical records for phenotyping youth depression.
Article in Evidence-based mental health, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 3 syntheses or guidelines pooled it, 78 citations in OpenAlex.
- Text-Based Depression Estimation Using Machine Learning With Standard Labels: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review.Artificial intelligence in medicine · 2023Pooled it
- Prediction and diagnosis of depression using machine learning with electronic health records data: a systematic review.BMC medical informatics and decision making · 2023Pooled it
- Identifying postpartum depression subtypes using natural language processing and clinical notes.BMJ mental health · 2026Article
- Natural language processing to identify suicidal ideation and anhedonia in major depressive disorder.BMC medical informatics and decision making · 2025Article
- Machine Learning for Mental Health: Applications, Challenges, and the Clinician's Role.Current psychiatry reports · 2024Review
- Thinking Aloud or Screaming Inside: Exploratory Study of Sentiment Around Work.JMIR formative research · 2022Article
- Integrating multimodal data through interpretable heterogeneous ensembles.bioRxiv : the preprint server for biology · 2022Article
- Article
- Application of Artificial Intelligence on Psychological Interventions and Diagnosis: An Overview.Frontiers in psychiatry · 2022Review
- Association between depressive symptoms and diagnosis of diabetes and its complications: A network analysis in electronic health records.Frontiers in psychiatry · 2022Article
- Integrating multimodal data through interpretable heterogeneous ensembles.Bioinformatics advances · 2022Article
- Natural language processing of clinical mental health notes may add predictive value to existing suicide risk models.Psychological medicine · 2021Article
- Digital Mental Health Challenges and the Horizon Ahead for Solutions.JMIR mental health · 2021Article
- Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence.Scientific reports · 2021Article
- Medical Text Classification Using Hybrid Deep Learning Models with Multihead Attention.Computational intelligence and neuroscience · 2021Article
- Comparing Deep Learning and Conventional Machine Learning Models for Predicting Mental Illness from History of Present Illness Notations.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- Use of a Machine Learning Program to Correctly Triage Incoming Text Messaging Replies From a Cardiovascular Text-Based Secondary Prevention Program: Feasibility Study.JMIR mHealth and uHealth · 2020Article
- Personalise antidepressant treatment for unipolar depression combining individual choices, risks and big data (PETRUSHKA): rationale and protocol.Evidence-based mental health · 2020Article
- Polar labeling: silver standard algorithm for training disease classifiers.Bioinformatics (Oxford, England) · 2020Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWe report a study of machine learning applied to the phenotyping of psychiatric diagnosis for research recruitment in youth depression, conducted with 861 labelled electronic medical records (EMRs) documents. A model was built that could accurately identify individuals who were suitable candidates for a study on youth depression.
objectiveOur objective was a model to identify individuals who meet inclusion criteria as well as unsuitable patients who would require exclusion.
methodsOur methods included applying a system that coded the EMR documents by removing personally identifying information, using two psychiatrists who labelled a set of EMR documents (from which the 861 came), using a brute force search and training a deep neural network for this task.
findingsAccording to a cross-validation evaluation, we describe a model that had a specificity of 97% and a sensitivity of 45% and a second model with a specificity of 53% and a sensitivity of 89%. We combined these two models into a third one (sensitivity 93.5%; specificity 68%; positive predictive value (precision) 77%) to generate a list of most suitable candidates in support of research recruitment.
conclusionOur efforts are meant to demonstrate the potential for this type of approach for patient recruitment purposes but it should be noted that a larger sample size is required to build a truly reliable recommendation system. CLINICAL IMPLICATIONS: Future efforts will employ alternate neural network algorithms available and other machine learning methods.
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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.