ArticleJMIR mental health2024
Machine Learning, Deep Learning, and Data Preprocessing Techniques for Detecting, Predicting, and Monitoring Stress and Stress-Related Mental Disorders: Scoping Review.
Article in JMIR mental health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 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
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- AI-Based Approaches to Refugee Mental Health Care: Systematic Integrative Review.Journal of medical Internet research · 2026Pooled it
- Machine learning approaches to anxiety detection: trends, model evaluation, and future directions.Frontiers in artificial intelligence · 2025Pooled it
- Explainable Machine Learning for Predicting Student Depression Risk.Bioengineering (Basel, Switzerland) · 2026Article
- Classifying mental stress from eye tracking data: deep learning approaches for out-of-the-lab conditions.Scientific reports · 2026Article
- Explainable Text-Based Depression and Suicide Risk Prediction from Social Media Using Deep Learning and Graph Neural Networks.Healthcare (Basel, Switzerland) · 2026Article
- Investigating Stress-Related Heart Rate Behavior and Rhythm in College Students Using Trend Analysis Methods.Sensors (Basel, Switzerland) · 2026Article
- Electrocardiogram-Based Mental Stress Detection Amid Everyday Activities Using Machine Learning: Model Development and Validation Study.Journal of medical Internet research · 2026Article
- Mental Stress Detection Using Physiological Sensors and Artificial Intelligence: A Review.Sensors (Basel, Switzerland) · 2026Review
- Development and validation of a machine learning-based risk prediction model for non-suicidal self-injury in adolescents.Frontiers in psychiatry · 2026Article
- Artificial Intelligence for Mental Health Monitoring: A Solution for Digital Behavioral Health Care and Education-An Umbrella Review.Health science reports · 2026Review
- Predicting the risk of mental disorders using complete blood count indicators: a machine learning approach.Frontiers in medicine · 2026Article
- Optimized federated learning framework with RegNetZ and Swin-Transformer for multimodal pancreatic cancer detection1.Scientific reports · 2025Article
- POC Sensor Systems and Artificial Intelligence-Where We Are Now and Where We Are Going?Biosensors · 2025Review
- Artificial intelligence in mental health: integrating opportunities and challenges of multimodal deep learning for mental disorder prevention and treatment.Annals of medicine and surgery (2012) · 2025Review
- Quantifying Mental Stress Using Cardiovascular Responses: A Scoping Review.Sensors (Basel, Switzerland) · 2025Article
- Artificial intelligence for difficult airway assessment: a protocol for a systematic review with meta-analysis.BMJ open · 2025Article
- Estimating the Prevalence of Schizophrenia in the General Population of Japan Using an Artificial Neural Network-Based Schizophrenia Classifier: Web-Based Cross-Sectional Survey.JMIR formative research · 2025Article
- Precision Assessment of Real-World Associations Between Stress and Sleep Duration Using Actigraphy Data Collected Continuously for an Academic Year: Individual-Level Modeling Study.JMIR formative research · 2024Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMental stress and its consequent mental health disorders (MDs) constitute a significant public health issue. With the advent of machine learning (ML), there is potential to harness computational techniques for better understanding and addressing mental stress and MDs. This comprehensive review seeks to elucidate the current ML methodologies used in this domain to pave the way for enhanced detection, prediction, and analysis of mental stress and its subsequent MDs.
objectiveThis review aims to investigate the scope of ML methodologies used in the detection, prediction, and analysis of mental stress and its consequent MDs.
methodsUsing a rigorous scoping review process with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, this investigation delves into the latest ML algorithms, preprocessing techniques, and data types used in the context of stress and stress-related MDs.
resultsA total of 98 peer-reviewed publications were examined for this review. The findings highlight that support vector machine, neural network, and random forest models consistently exhibited superior accuracy and robustness among all ML algorithms examined. Physiological parameters such as heart rate measurements and skin response are prevalently used as stress predictors due to their rich explanatory information concerning stress and stress-related MDs, as well as the relative ease of data acquisition. The application of dimensionality reduction techniques, including mappings, feature selection, filtering, and noise reduction, is frequently observed as a crucial step preceding the training of ML algorithms.
conclusionsThe synthesis of this review identified significant research gaps and outlines future directions for the field. These encompass areas such as model interpretability, model personalization, the incorporation of naturalistic settings, and real-time processing capabilities for the detection and prediction of stress and stress-related MDs.
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.