SynthesisBrain and behavior2025
Accuracy of Machine Learning in Predicting Post-Stroke Depression: A Systematic Review and Meta-Analysis.
Synthesis in Brain and behavior, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- Factors associated with post-stroke depression one month after discharge among older patients based on Health Ecology Theory: a retrospective cross-sectional study of consecutive cases.Frontiers in psychiatry · 2026Article
- Predicting Post-Stroke Depression Risk in Elderly Patients Based on Machine Learning: A Retrospective Cohort Study Integrating Neuroimaging and Psychosocial Variables.Neuropsychiatric disease and treatment · 2026Article
- Prevalence, associated factors, and machine learning-based prediction of probable depression among individuals with chronic diseases in Bangladesh.BMC psychiatry · 2025Article
- Toward precision psychological rehabilitation: predicting CBT efficacy in post-stroke depression using machine learning.Frontiers in psychiatry · 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionPost-stroke depression is one of the important complications of stroke and affects patients' quality of life. Early identification of post-stroke depression is crucial for its timely prevention. The accuracy of machine learning as a prediction method is controversial. To systematically analyze these studies, we conducted a systematic evaluation to review the effectiveness of the machine learning prediction models in predicting post-stroke depression based on meta-analysis.
methodsAs of November 20, 2023, we conducted a systematic literature retrieval in databases such as PubMed, Embase, Cochrane, and Web of Science to investigate machine learning model predictions in patients who had post-stroke depression. The tool used in this study to assess the quality of the retrieved literature is PROBAST (Prediction model Risk of Bias Assessment Tool).
resultsA total of 28 studies with 85,223 patients as subjects were included in the meta-analysis. According to data, the c-index in the validation set was significantly lower than that in the training set and may have been at risk of overfitting, even though there was a desirable accuracy. In addition, we found substantial differences in the duration of follow-up for post-stroke depression, and meta-regression showed that the c-index based on the prediction models did not decay with longer follow-up.
conclusionsReasonable prediction models are effective prediction tools for post-stroke depression. A reasonable prediction seems to predict the risk of post-stroke depression occurrence at different time points and can provide a prevention tool specific to the risk.
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.