Implementing machine learning in bipolar diagnosis in China

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

doi:10.1038/s41398-019-0638-8

Abstract

Bipolar 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.

Highlights

Bipolar disorder (BPD) is characterized by recurrent depression and mania/hypomania[1]
The healthy control (HC) (N = 228) subjects were recruited among people who responded to flyers distributed near the participating health centers
least absolute shrinkage and selection operator (LASSO) and linear discriminant analysis (LDA) behaved quite similar for the major depressive disorder (MDD) and BPD models, but LDA outperformed LASSO for the HC model

Summary

Introduction

Bipolar disorder (BPD) is characterized by recurrent depression and mania/hypomania[1]. Difficulties and delays in the diagnosis of BPD impede the effective treatment of patients. BPD is prone to misdiagnosis as major depressive disorder (MDD). Despite being one of the 10 most debilitating noncommunicable diseases[2,3], BPD is misdiagnosed as recurrent MDD in 60% of patients seeking treatment for depression[4]. The recent 3rd national Chinese Mental Health Survey (CMHS) reported. There is an urgent need to improve the early diagnosis of BPD, especially in terms of distinguishing patients with BPD from those with MDD. In light of the current large number of domestic patient diagnoses, easier and targeted diagnostic evaluation tools are needed

Objectives

Methods

Results

Conclusion