Chia Nan University of Pharmacy & Science Institutional Repository:Item 310902800/34874
English  |  正體中文  |  简体中文  |  Items with full text/Total items : 18269/20496 (89%)
Visitors : 9328086      Online Users : 783
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version


    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://ir.cnu.edu.tw/handle/310902800/34874


    题名: Predicting new-onset post-stroke depression from real-world data using machine learning algorithm
    作者: Chen, Yu-Ming
    Chen, Po-Cheng
    Lin, Wei-Che
    Hung, Kuo-Chuan
    Chen, Yang-Chieh Brian
    Hung, Chi-Fa
    Wang, Liang-Jen
    Wu, Ching-Nung
    Hsu, Chih-Wei
    Kao, Hung-Yu
    贡献者: Chang Gung Univ, Kaohsiung Chang Gung Mem Hosp, Dept Psychiat, Coll Med
    Chang Gung Univ, Kaohsiung Chang Gung Mem Hosp, Dept Phys Med & Rehabil, Coll Med
    Chang Gung Univ, Kaohsiung Chang Gung Mem Hosp, Dept Diagnost Radiol, Coll Med
    Chi Mei Med Ctr, Dept Anesthesiol
    Chia Nan Univ Pharm & Sci, Dept Hosp & Hlth Care Adm, Coll Recreat & Hlth Management
    Natl Sun Yat Sen Univ, Coll Med, Sch Med, Kaohsiung
    Natl Pingtung Univ Sci & Technol, Coll Humanities & Social Sci
    Chang Gung Univ, Kaohsiung Chang Gung Mem Hosp, Dept Child & Adolescent Psychiat, Coll Med
    Chang Gung Univ, Kaohsiung Chang Gung Mem Hosp, Dept Otolaryngol, Coll Med
    Natl Cheng Kung Univ, Coll Med, Dept Publ Hlth
    Natl Cheng Kung Univ, Dept Comp Sci & Informat Engn
    关键词: artificial intelligence
    depressive disorder
    electronic medical record
    feature importance
    prediction
    日期: 2023
    上传时间: 2024-12-25 11:04:55 (UTC+8)
    出版者: FRONTIERS MEDIA SA
    摘要: IntroductionPost-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. MethodsWe 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. ResultsIn 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. DiscussionMachine 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.
    關聯: Frontiers in Psychiatry, v.14, Article 1195586
    显示于类别:[Dept. of Hospital and Health (including master's program)] Periodical Articles

    文件中的档案:

    档案 描述 大小格式浏览次数
    fpsyt.2023.1195586.pdf645KbAdobe PDF58检视/开启
    index.html0KbHTML194检视/开启


    在CNU IR中所有的数据项都受到原著作权保护.

    TAIR相关文章

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈