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Related Experiment Video

Updated: Jan 7, 2026

A Middle Cerebral Artery Occlusion Technique for Inducing Post-stroke Depression in Rats
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Clinical prediction models for post-stroke depression: a systematic review and meta-analysis.

Honggang Wu1, Yating Xiao2, Xin Hu1

  • 1Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China.

Frontiers in Psychiatry
|January 2, 2026
PubMed
Summary

Machine learning models show promise for predicting post-stroke depression (PSD), with Neural Networks performing best. Functional, physical, and cognitive assessments are key data sources for accurate PSD prediction.

Keywords:
PROBAST+AIartificial intelligencedepression predictionmachine learning modelsmeta-analysispost-stroke depressionsystematic review

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Area of Science:

  • Neuroscience
  • Medical Informatics
  • Psychiatry

Background:

  • Post-stroke depression (PSD) is a common and serious complication following a stroke.
  • PSD is linked to worse cognitive function, increased disability, and higher mortality rates.
  • Early identification of PSD is crucial for effective treatment and improved patient outcomes.

Purpose of the Study:

  • To systematically review and evaluate the effectiveness of various clinical prediction models for post-stroke depression (PSD).
  • To compare the performance of traditional statistical methods versus machine learning algorithms in predicting PSD.
  • To identify the most accurate data sources for PSD prediction models.

Main Methods:

  • A systematic review and meta-analysis of 16 studies published from 2000 to present.
  • Searched major databases (PubMed, Embase, Cochrane, Web of Science).
  • Assessed model risk of bias using PROBAST+AI and analyzed predictive accuracy via Area Under the Curve (AUC).

Main Results:

  • Neural Network models showed the highest pooled AUC (0.88), though based on limited data.
  • Logistic Regression, Decision Tree, and K-Nearest Neighbor models demonstrated competitive AUCs (0.77-0.83).
  • Functional, physical, and cognitive assessments yielded the highest predictive accuracy (AUC=0.86), outperforming biomarker models (AUC=0.80).

Conclusions:

  • Machine learning, especially Neural Networks, shows potential for PSD prediction, but requires more robust evidence.
  • Traditional models offer stable performance, and functional/cognitive assessments are strong predictors.
  • Further high-quality, prospective, multicenter studies are needed to validate reliable PSD prediction models.