ArticleScientific reports2020
Using machine learning-based analysis for behavioral differentiation between anxiety and depression.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Key predictors of postpartum depression and anxiety symptoms among mothers in Kilifi, Kenya: a machine learning approach.Frontiers in psychiatry · 2026Article
- Development and Validation of a Risk Screening Model for Depressive Symptoms in Older Inpatients: A Cross-Sectional Study.Clinical interventions in aging · 2026Article
- Identifying psychiatric manifestations in outpatients with depression and anxiety: a large language model-based approach.Npj mental health research · 2025Article
- Explainable AI for Depression Detection and Severity Classification From Activity Data: Development and Evaluation Study of an Interpretable Framework.JMIR mental health · 2025Article
- Use of Artificial Intelligence in Psychiatric Research and Practice: A Qualitative Interview Study with Experts from Psychiatry, Computer Science and Philosophy in Germany.European psychiatry : the journal of the Association of European Psychiatrists · 2025Article
- Evaluating the reliability of the Word-Sentence Association Paradigm (WSAP) as an interpretation bias assessment across ethnoracial groups.Cognitive therapy and research · 2025Article
- Longitudinal observation of psychophysiological data as a novel approach to personalised postural defect rehabilitation.Scientific reports · 2025Article
- Machine learning meta-analysis identifies individual characteristics moderating cognitive intervention efficacy for anxiety and depression symptoms.NPJ digital medicine · 2025Article
- Cognitive biases: potential behavioral marker for future development of postpartum depression and childbirth-related post-traumatic stress disorder.Frontiers in psychiatry · 2025Article
- Detecting depression severity using weighted random forest and oxidative stress biomarkers.Scientific reports · 2024Article
- A novel approach to anxiety level prediction using small sets of judgment and survey variables.Npj mental health research · 2024Article
- Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression.Diagnostics (Basel, Switzerland) · 2023Review
- Precision medicine in oncology - machine learning recommendations.American journal of cancer research · 2023Article
- Spider vs. guns: expectancy and attention biases to phylogenetic threat do not extend to ontogenetic threat.Frontiers in psychology · 2023Article
- Impact of mobile connectivity on students' wellbeing: Detecting learners' depression using machine learning algorithms.PloS one · 2023Article
- Depression Detection Based on Hybrid Deep Learning SSCL Framework Using Self-Attention Mechanism: An Application to Social Networking Data.Sensors (Basel, Switzerland) · 2022Article
- Assessment and Prediction of Depression and Anxiety Risk Factors in Schoolchildren: Machine Learning Techniques Performance Analysis.JMIR formative research · 2022Article
- A Machine Learning Approach for Continuous Mining of Nonidentifiable Smartphone Data to Create a Novel Digital Biomarker Detecting Generalized Anxiety Disorder: Prospective Cohort Study.JMIR medical informatics · 2022Article
- Internalized Weight Stigma, Psychological Well-Being, and Sleep in Women.International journal of behavioral medicine · 2022Article
- How Social Experiences Affect Interpretation Bias Among Individuals With Non-clinical Depression: The Role of Ostracism.Frontiers in psychiatry · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Anxiety and depression are distinct-albeit overlapping-psychiatric diseases, currently diagnosed by self-reported-symptoms. This research presents a new diagnostic methodology, which tests rigorously for differences in cognitive biases among subclinical anxious and depressed individuals. 125 participants were divided into four groups based on the levels of their anxiety and depression symptoms. A comprehensive behavioral test battery detected and quantified various cognitive-emotional biases. Advanced machine-learning tools, developed for this study, analyzed these results. These tools detect unique patterns that characterize anxiety versus depression to predict group membership. The prediction model for differentiating between symptomatic participants (i.e., high symptoms of depression, anxiety, or both) compared to the non-symptomatic control group revealed a 71.44% prediction accuracy for the former (sensitivity) and 70.78% for the latter (specificity). 68.07% and 74.18% prediction accuracy was obtained for a two-group model with high depression/anxiety, respectively. The analysis also disclosed which specific behavioral measures contributed to the prediction, pointing to key cognitive mechanisms in anxiety versus depression. These results lay the ground for improved diagnostic instruments and more effective and focused individually-based treatment.
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