TY - JOUR
T1 - Predicting new-onset post-stroke depression from real-world data using machine learning algorithm
AU - Chen, Yu Ming
AU - Chen, Po Cheng
AU - Lin, Wei Che
AU - Hung, Kuo Chuan
AU - Chen, Yang Chieh Brian
AU - Hung, Chi Fa
AU - Wang, Liang Jen
AU - Wu, Ching Nung
AU - Hsu, Chih Wei
AU - Kao, Hung Yu
N1 - Funding Information:
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.
Publisher Copyright:
Copyright © 2023 Chen, Chen, Lin, Hung, Chen, Hung, Wang, Wu, Hsu and Kao.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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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U2 - 10.3389/fpsyt.2023.1195586
DO - 10.3389/fpsyt.2023.1195586
M3 - Article
AN - SCOPUS:85164437771
SN - 1664-0640
VL - 14
JO - Frontiers in Psychiatry
JF - Frontiers in Psychiatry
M1 - 1195586
ER -