Implementing machine learning in bipolar diagnosis in China

Author:

Ma YantaoORCID,Ji Jun,Huang Yun,Gao Huimin,Li Zhiying,Dong Wentian,Zhou Shuzhe,Zhu Yue,Dang Weimin,Zhou Tianhang,Yu Haiqing,Yu Bin,Long Yuefeng,Liu Long,Sachs Gary,Yu Xin

Abstract

AbstractBipolar disorder (BPD) is often confused with major depression, and current diagnostic questionnaires are subjective and time intensive. The aim of this study was to develop a new Bipolar Diagnosis Checklist in Chinese (BDCC) by using machine learning to shorten the Affective Disorder Evaluation scale (ADE) based on an analysis of registered Chinese multisite cohort data. In order to evaluate the importance of each item of the ADE, a case-control study of 360 bipolar disorder (BPD) patients, 255 major depressive disorder (MDD) patients and 228 healthy (no psychiatric diagnosis) controls (HCs) was conducted, spanning 9 Chinese health facilities participating in the Comprehensive Assessment and Follow-up Descriptive Study on Bipolar Disorder (CAFÉ-BD). The BDCC was formed by selected items from the ADE according to their importance as calculated by a random forest machine learning algorithm. Five classical machine learning algorithms, namely, a random forest algorithm, support vector regression (SVR), the least absolute shrinkage and selection operator (LASSO), linear discriminant analysis (LDA) and logistic regression, were used to retrospectively analyze the aforementioned cohort data to shorten the ADE. Regarding the area under the receiver operating characteristic (ROC) curve (AUC), the BDCC had high AUCs of 0.948, 0.921, and 0.923 for the diagnosis of MDD, BPD, and HC, respectively, despite containing only 15% (17/113) of the items from the ADE. Traditional scales can be shortened using machine learning analysis. By shortening the ADE using a random forest algorithm, we generated the BDCC, which can be more easily applied in clinical practice to effectively enhance both BPD and MDD diagnosis.

Funder

Capital Health Research and Development of Special

National Natural Science Foundation of China

Natural Science Foundation of Shandong Province

Publisher

Springer Science and Business Media LLC

Subject

Biological Psychiatry,Cellular and Molecular Neuroscience,Psychiatry and Mental health

Reference31 articles.

1. Phillips, M. L. & Kupfer, D. J. Bipolar disorder diagnosis: challenges and future directions. Lancet 381, 1663–1671 (2013).

2. Mathers, C. The global burden of disease: 2004 update. (World Health Organization, 2008).

3. Lozano, R. et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. The Lancet. 380, 2095–2128 (2012).

4. Goodwin, F. K. & Jamison, K. R. Manic-Depressive Illness: Bipolar Disorders and Recurrent Depression, vol. 1 (Oxford University Press, 2007).

5. Huang, Y. et al. Prevalence of mental disorders in China: a cross-sectional epidemiological study. Lancet Psychiatry 6, 211–224 (2019).

全球学者库

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"全球学者库"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前全球学者库共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2023 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3