Implementing machine learning in bipolar diagnosis in China

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.

The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review

“…In 9 (28%) of the 33 studies, SVM-based models were used to diagnose BD (specific types are not mentioned) [ 18 - 26 ]. In 1 study [ 18 ], this model was used to diagnose chronic BD and first-episode BD, whereas in 3 studies [ 19 , 21 , 26 ], SVM was used to diagnose type 1 and type 2 BD. However, SVM [ 24 ] was also used to diagnose unspecified types of BD.…”

“…Linear regression models were used in 3 (9.09%) studies [ 33 , 34 , 47 ] to diagnose type 1, type 2, and unspecified BD. In 2 (6.06%) studies [ 33 , 47 ], the elastic net method and least absolute shrinkage and selection operator (LASSO) [ 19 , 34 ] were used for diagnosing of type I, type II, and other unspecified BD types.…”

“…Linear discriminant analysis (LDA) and growth mixture modeling (GMM) were employed in 2 (6.06%) studies [ 19 , 41 ] for diagnosing type 1 and type 2 BD.…”

“…As shown in Table 4 and Multimedia Appendix 4 , clinical and nonclinical data are reported in the included articles. The following data sets were collected from various public and private sources: clinical data (immune-inflammatory signature, blood sample, neuropsychological, neurocognitive, electroencephalography and PGBI-10M manic symptom data) [ 18 - 28 , 30 , 31 , 33 , 38 , 41 - 43 , 46 , 49 ] and nonclinical data (Cambridge Neuropsychological Test Automated Battery cognitive scores, microarray expression data sets, large-scale GWAS, fractional anisotropy, axial diffusivity, radial diffusivity, electronic medical records, bipolarity indices, affective disorder evaluation scale, daily mood ratings survey and phenotypic data sets) [ 14 , 19 , 21 , 24 , 29 , 32 , 34 , 36 , 37 , 45 , 47 , 49 , 50 ].…”

“…The accuracy level ranged from ≤70% to >91%. The accuracy level was ≤70% in 3 studies [ 25 , 32 , 36 ], 71%-78% in 7 studies [ 18 , 23 , 34 , 37 , 43 , 47 , 49 ], 83%-90% in 9 studies [ 14 , 20 , 21 , 26 , 28 , 29 , 31 , 33 , 42 ], and >91% in 5 studies [ 19 , 22 , 24 , 35 , 40 ]. The highest accuracy was 98%, found in only 1 study, whereas the lowest accuracy level was 64%.…”