SynthesisBMC medical informatics and decision making2023
Prediction and diagnosis of depression using machine learning with electronic health records data: a systematic review.
Synthesis in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
What it found
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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
35 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Recent developments in omics studies and artificial intelligence in depression and suicide.Translational psychiatry · 2025Pooled 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
- Prediction of self-harm in people with newly-diagnosed depression: development and validation of risk prediction models.Molecular psychiatry · 2026Article
- Whose Truth Is Ground Truth?: Consequences of Label Choice on Machine Learning Models Predicting Depression.Research square · 2026Article
- Frailty-Driven Prediction of Inpatient Obstructive Sleep Apnea and Related Sleep Disorder Diagnoses Using Explainable AI.Biomedicines · 2026Article
- Integrating epidemiologic modeling and explainable machine learning to predict and identify factors associated with self-reported depression among adults in Tennessee, United States.Discover mental health · 2026Article
- Polygenic risk for depression is associated with post-stroke depression and stroke.IBRO neuroscience reports · 2026Article
- Enhancing Depression Identification and Stratification with a Claims-Based Analytical Framework.The journal of behavioral health services & research · 2026Article
- ClinPreAI: An Agentic AI System for Early Postpartum Depression Risk Prediction from Multimodal EHR Data.medRxiv : the preprint server for health sciences · 2026Article
- Interpretable Feature Selection and Hybrid Deep Learning Models for Depressive Symptoms Prediction from Wearable Device Data.Journal of medical systems · 2026Article
- A multimodal approach to depression diagnosis: insights from machine learning algorithm development in primary care.European archives of psychiatry and clinical neuroscience · 2026Article
- Who's at risk for emergent depression years later? Predictive modeling in a nine-year longitudinal cohort.BMC psychiatry · 2026Article
- Optimization of potential targets for antidepressant Chinese medicines: AI and multi-omics methods.Chinese medicine · 2026Review
- Machine Learning-Based Classification of Depression Using Inflammatory Biomarkers in Pancreatic Cancer Patients.The Kaohsiung journal of medical sciences · 2026Article
- Machine learning-based identification of anxiety symptoms in Chinese community-dwelling older adults: a comparative study of six algorithms with SHAP analysis and nomogram development.Frontiers in psychiatry · 2026Article
- Machine Learning-Based Depression Risk Prediction Models for Older Adults Analyzing From the Perspective of the Health Ecology Model: A Scoping Review.Neuropsychiatric disease and treatment · 2026Review
- Detection of depression risk among older adults using home-deployed socially assistive robots: a real-world study.Frontiers in psychiatry · 2026Article
- Deep learning with ensemble-based hybrid AI model for bipolar and unipolar depression detection using demographic and behavioral based on time-series data.Dialogues in clinical neuroscience · 2025Article
- Systematic Review and Meta-Analysis of Explainable Machine Learning Models for Clinical Depression Detection.Behavioral sciences (Basel, Switzerland) · 2025Review
- Predicting 3-year depressive symptoms among middle-aged and older adults in rural China using random forest: insights from the China health and retirement longitudinal study.BMC psychology · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundDepression is one of the most significant health conditions in personal, social, and economic impact. The aim of this review is to summarize existing literature in which machine learning methods have been used in combination with Electronic Health Records for prediction of depression.
methodsSystematic literature searches were conducted within arXiv, PubMed, PsycINFO, Science Direct, SCOPUS and Web of Science electronic databases. Searches were restricted to information published after 2010 (from 1st January 2011 onwards) and were updated prior to the final synthesis of data (27th January 2022).
resultsFollowing the PRISMA process, the initial 744 studies were reduced to 19 eligible for detailed evaluation. Data extraction identified machine learning methods used, types of predictors used, the definition of depression, classification performance achieved, sample size, and benchmarks used. Area Under the Curve (AUC) values more than 0.9 were claimed, though the average was around 0.8. Regression methods proved as effective as more developed machine learning techniques. LIMITATIONS: The categorization, definition, and identification of the numbers of predictors used within models was sometimes difficult to establish, Studies were largely Western Educated Industrialised, Rich, Democratic (WEIRD) in demography.
conclusionThis review supports the potential use of machine learning techniques with Electronic Health Records for the prediction of depression. All the selected studies used clinically based, though sometimes broad, definitions of depression as their classification criteria. The reported performance of the studies was comparable to or even better than that found in primary care. There are concerns with generalizability and interpretability.
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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.