Predicting new-onset post-stroke depression from real-world data using machine learning algorithm

Yu Ming Chen, Po Cheng Chen, Wei Che Lin, Kuo Chuan Hung, Yang Chieh Brian Chen, Chi Fa Hung, Liang Jen Wang, Ching Nung Wu, Chih Wei Hsu, Hung Yu Kao

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Article number1195586
JournalFrontiers in Psychiatry
Publication statusPublished - 2023

All Science Journal Classification (ASJC) codes

  • Psychiatry and Mental health


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