ArticleFrontiers in psychiatry2023
Predicting new-onset post-stroke depression from real-world data using machine learning algorithm.
Article in Frontiers in psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 3 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 3 syntheses or guidelines pooled it, 25 citations in OpenAlex.
- Accuracy of Machine Learning in Predicting Post-Stroke Depression: A Systematic Review and Meta-Analysis.Brain and behavior · 2025Pooled it
- Research progress of quantitative electroencephalography in post-ischemic stroke mental disorders.Frontiers in neurology · 2025Pooled it
- Clinical prediction models for post-stroke depression: a systematic review and meta-analysis.Frontiers in psychiatry · 2025Pooled it
- Predictive factors for remission in post-stroke depression: A Markov model cohort study.iScience · 2026Article
- Using SHAP and LIME to Explain Machine Learning Models Predicting Comorbid Depression and Stroke From Daily Dietary Nutrient Intake in a US Population-Based Study.Food science & nutrition · 2026Article
- Predicting five-year comorbid bipolar disorder after attention-deficit/hyperactivity disorder diagnosis: a population-based machine learning approach.Child and adolescent psychiatry and mental health · 2025Article
- Development and interpretation of a machine learning risk prediction model for post-stroke depression in a Chinese population.Scientific reports · 2025Article
- Automated Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Deep Learning Methods and Sequential Data.Bioengineering (Basel, Switzerland) · 2025Article
- Identifying individuals at risk of post-stroke depression: Development and validation of a predictive model.Saudi medical journal · 2025Article
- Cardiac history and post-stroke depression association in Chinese stroke survivors: a cross sectional study.Scientific reports · 2025Article
- Network-based predictive models for artificial intelligence: an interpretable application of machine learning techniques in the assessment of depression in stroke patients.BMC geriatrics · 2025Article
- Influencing factors and predictive models of early post-stroke depression in patients with acute ischemic stroke.BMC neurology · 2025Article
- Machine Learning Models and Applications for Early Detection.Sensors (Basel, Switzerland) · 2024Review
- Exploring the multifaceted potential of (R)-ketamine beyond antidepressant applications.Frontiers in pharmacology · 2024Review
- Risk Factors for Post-Stroke Depression Following the Lifting of COVID-19 Restrictions.International journal of general medicine · 2024Article
- Explainable Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Machine Learning Techniques in a Population of 1780 Patients.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Post-stroke depression (PSD) is a serious mental disorder after ischemic stroke. Early detection is important for clinical practice. This research aims to develop machine learning models to predict new-onset PSD using real-world data. Methods: We collected data for ischemic stroke patients from multiple medical institutions in Taiwan between 2001 and 2019. We developed models from 61,460 patients and used 15,366 independent patients to test the models' performance by evaluating their specificities and sensitivities. The predicted targets were whether PSD occurred at 30, 90, 180, and 365 days post-stroke. We ranked the important clinical features in these models. Results: In the study's database sample, 1.3% of patients were diagnosed with PSD. The average specificity and sensitivity of these four models were 0.83-0.91 and 0.30-0.48, respectively. Ten features were listed as important features related to PSD at different time points, namely old age, high height, low weight post-stroke, higher diastolic blood pressure after stroke, no pre-stroke hypertension but post-stroke hypertension (new-onset hypertension), post-stroke sleep-wake disorders, post-stroke anxiety disorders, post-stroke hemiplegia, and lower blood urea nitrogen during stroke. Discussion: Machine learning models can provide as potential predictive tools for PSD and important factors are identified to alert clinicians for early detection of depression in high-risk stroke patients.
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