ReviewNpj mental health research2023
Systematic review of machine learning in PTSD studies for automated diagnosis evaluation.
Review in Npj mental health research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Prediction models for postpartum post-traumatic stress disorder: a systematic review and meta-analysis.Archives of women's mental health · 2026Pooled it
- Linguistic markers for identifying post-traumatic stress disorder and associated symptoms: a systematic literature review.Journal of the American Medical Informatics Association : JAMIA · 2025Pooled it
- Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis.BMC medical informatics and decision making · 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
- Quantitative Electroencephalography as a Complement to Symptom-Based Psychiatric Diagnosis: A Narrative Review.Journal of personalized medicine · 2026Review
- A systematic review on mindfulness-based immersive interventions in depressive disorders.Npj mental health research · 2026Review
- WiseMind: a knowledge-guided multi-agent framework for accurate and empathetic psychiatric diagnosis.NPJ digital medicine · 2026Article
- P300 Spatiotemporal Prior-Based Transformer-CNN for Auxiliary Diagnosis of PTSD.Brain sciences · 2025Article
- ECG Signal Analysis for Detection and Diagnosis of Post-Traumatic Stress Disorder: Leveraging Deep Learning and Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2025Article
- Assessment of PTSD in military personnel via machine learning based on physiological habituation in a virtual immersive environment.Scientific reports · 2025Article
- Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications.Journal of clinical medicine · 2025Review
- Novel psychedelic interventions for post-traumatic stress disorder and their promise for precision medicine.Therapeutic advances in psychopharmacology · 2025Review
- A systematic review of machine learning findings in PTSD and their relationships with theoretical models.Nature. Mental health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Post-traumatic stress disorder (PTSD) is frequently underdiagnosed due to its clinical and biological heterogeneity. Worldwide, many people face barriers to accessing accurate and timely diagnoses. Machine learning (ML) techniques have been utilized for early assessments and outcome prediction to address these challenges. This paper aims to conduct a systematic review to investigate if ML is a promising approach for PTSD diagnosis. In this review, statistical methods were employed to synthesize the outcomes of the included research and provide guidance on critical considerations for ML task implementation. These included (a) selection of the most appropriate ML model for the available dataset, (b) identification of optimal ML features based on the chosen diagnostic method, (c) determination of appropriate sample size based on the distribution of the data, and (d) implementation of suitable validation tools to assess the performance of the selected ML models. We screened 3186 studies and included 41 articles based on eligibility criteria in the final synthesis. Here we report that the analysis of the included studies highlights the potential of artificial intelligence (AI) in PTSD diagnosis. However, implementing AI-based diagnostic systems in real clinical settings requires addressing several limitations, including appropriate regulation, ethical considerations, and protection of patient privacy.
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.