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

doi:10.3389/fpsyt.2023.1195586

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

Front Psychiatry. 2023 Jun 19:14:1195586. doi: 10.3389/fpsyt.2023.1195586. eCollection 2023.

Authors

Yu-Ming Chen^#¹, Po-Cheng Chen^#², Wei-Che Lin³, Kuo-Chuan Hung^{4

5}, Yang-Chieh Brian Chen¹, Chi-Fa Hung^{1

6

7}, Liang-Jen Wang⁸, Ching-Nung Wu^{9

10}, Chih-Wei Hsu^{1

11}, Hung-Yu Kao¹¹

Affiliations

¹ Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan.
² Department of Physical Medicine and Rehabilitation, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan.
³ Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan.
⁴ Department of Anesthesiology, Chi Mei Medical Center, Tainan City, Taiwan.
⁵ Department of Hospital and Health Care Administration, College of Recreation and Health Management, Chia Nan University of Pharmacy and Science, Tainan City, Taiwan.
⁶ School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan.
⁷ College of Humanities and Social Sciences, National Pingtung University of Science and Technology, Pingtung, Taiwan.
⁸ Department of Child and Adolescent Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan.
⁹ Department of Otolaryngology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan.
¹⁰ Department of Public Health, College of Medicine, National Cheng Kung University, Tainan City, Taiwan.
¹¹ Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan City, Taiwan.

^# Contributed equally.

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.

Keywords: artificial intelligence; depressive disorder; electronic medical record; feature importance; prediction.

Grants and funding

This study was supported by grants from the Chang Gung Medical Research Project (grant number CMRPG8M0531). The funding sources had no role in the design of the study.