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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.

In this study, red, green, cyan, warm white and cool white (RGCWW) LEDs are individually controlled to simulate dynamic daylight. The spectral power distributions (SPDs) are measured for both the daylight and the mixed white light. The correlated color temperature (CCT), circadian action factor (CAF) curves in the daytime of the mixed white LED almost overlap to those of daylight with a small error of about 1%. The CIE chromaticity coordinates of the mixed white light are distributed along the Planckian locus with a little deviation below 0.003. Also, its color rendering index (CRI) is above 90. The RGCWW five-chip LEDs of high circadian and visual performances will benefit to synchronize circadian rhythm in closed environment during long time without daylight, combined with better mood and alertness. The simulation errors and development trend of the mixed lighting are discussed.