ArticleScientific reports2021
Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 4 of them syntheses 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
78 citing papers in PubMed, 4 syntheses or guidelines pooled it, 209 citations in OpenAlex.
- Applying AI in the Context of the Association Between Device-Based Assessment of Physical Activity and Mental Health: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- Artificial intelligence as a predictive tool for mental health status: Insights from a systematic review and meta-analysis.PloS one · 2025Pooled it
- How do machine learning models perform in the detection of depression, anxiety, and stress among undergraduate students? A systematic review.Cadernos de saude publica · 2024Pooled 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
- Interpretable machine learning for depression symptom classification in NHANES: Performance in a curated high-confidence corpus and the full survey population.IBRO neuroscience reports · 2026Article
- Investigation of Machine Learning Models for Anxiety Levels Assessment in Patients With Breast Cancer Using Self-Questionnaires.Journal of Korean medical science · 2026Article
- Investigating the Impact of Fast-Paced Digital Content, Social Media Engagement, and Lifestyle Factors on Attention, Cognitive Load, and Behavioral Impulsivity: A Machine Learning Approach.Brain and behavior · 2026Article
- Whose Truth Is Ground Truth?: Consequences of Label Choice on Machine Learning Models Predicting Depression.Research square · 2026Article
- Pediatric precision sleep network: a study protocol for identifying sleep signatures of mental health risk in peri-adolescents.BMC pediatrics · 2026Article
- Predicting Anxiety in Individuals with Diabetes: A Comparative Analysis of Machine Learning Algorithms.Problemy endokrinologii · 2026Article
- Screening for depression risk via smartphone narratives with fully fine-tuned WavLM.Scientific reports · 2026Article
- Predicting Dental Anxiety and Cooperative Behavior in Children Using Machine Learning: A Cross-Sectional Predictive Modeling Study.Dentistry journal · 2026Article
- Smart Devices and Multimodal Systems for Mental Health Monitoring: From Theory to Application.Bioengineering (Basel, Switzerland) · 2026Review
- Association Between New Tea Drink Consumption and Mental Health Disorders Among Chinese College Students: An Interpretable Machine Learning Approach.Psychology research and behavior management · 2026Article
- Cultural validation of the RCADS and use of ensemble learning for symptom profiling of anxiety and depression.Frontiers in psychiatry · 2026Article
- Contribution of social determinants to symptoms of generalized anxiety disorder.PLOS mental health · 2026Article
- Lifestyle, psychological and demographic predictors of anxiety: insights from a large-scale survey and machine learning analysis.Frontiers in psychiatry · 2026Article
- Constructing a Bayesian neural network model for the cross-sectional classification of psychological distress among older adults: comprehensive analysis of humor styles, communication skills, and physical activity experiences.Frontiers in research metrics and analytics · 2026Article
- Explainable AI models for identifying anxiety and distress in cardiac patients with ICDs.BMC medical informatics and decision making · 2025Article
- Systematic Review and Meta-Analysis of Explainable Machine Learning Models for Clinical Depression Detection.Behavioral sciences (Basel, Switzerland) · 2025Review
18 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Generalized anxiety disorder (GAD) and major depressive disorder (MDD) are highly prevalent and impairing problems, but frequently go undetected, leading to substantial treatment delays. Electronic health records (EHRs) collect a great deal of biometric markers and patient characteristics that could foster the detection of GAD and MDD in primary care settings. We approached the problem of predicting MDD and GAD using a novel machine learning pipeline to re-analyze data from an observational study. The pipeline constitutes an ensemble of algorithmically distinct machine learning methods, including deep learning. A sample of 4,184 undergraduate students completed the study, undergoing a general health screening and completing a psychiatric assessment for MDD and GAD. After explicitly excluding all psychiatric information, 59 biomedical and demographic features from the general health survey in addition to a set of engineered features were used for model training. We assessed the model's performance on a held-out test set and found an AUC of 0.73 (sensitivity: 0.66, specificity: 0.7) and 0.67 (sensitivity: 0.55, specificity: 0.7) for GAD, and MDD, respectively. Additionally, we used advanced techniques (SHAP values) to illuminate which features had the greatest impact on prediction for each disease. The top predictive features for MDD were being satisfied with living conditions and having public health insurance. The top predictive features for GAD were vaccinations being up to date and marijuana use. Our results indicate moderate predictive performance for the application of machine learning methods in detection of GAD and MDD based on EHR data. By identifying important predictors of GAD and MDD, these results may be used in future research to aid in the early detection of MDD and GAD.
Indexed as
Identifiers
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.