Predominant polarity classification and associated clinical variables in bipolar disorder: A machine learning approach
Predominant polarity classification and associated clinical variables in bipolar disorder: A machine learning approach
复制标题
DOI：
10.1016/j.jad.2018.11.051
复制
发表时间：
2019-02-15
影响因子：
6.6
通讯作者：
Dias, Rodrigo da Silva
中科院分区：
文献类型：
--
作者：
Belizario, Gabriel Okawa;Borges Junior, Renato Gomes;Dias, Rodrigo da Silva
背景：双相情感障碍（BD）是一种严重的精神疾病，其特征是周期性发作的躁狂和抑郁障碍。优势极性（PP）似乎是BD的一个重要说明。本研究采用机器学习（ML）算法来准确地确定患者的PP，而不包括过去发作的数量和极性，同时探索PP与人口统计学和临床变量之间的关联。方法：从148例BD患者的队列中，使用定制问卷和SCID-CV收集人口统计学和临床变量。采用的算法是随机森林方法。该算法被编程，以将患者分为抑郁或躁狂为主的极性，并揭示哪些变量与specifier.Results相关：该算法达到了74.72%（95%CI = 72.29-77.15%）的AUC ROC的患者分为躁狂或抑郁PP。该算法选择的变量为：（1）首次抑郁发作时的年龄;（2）住院次数;（3）BD II型;（4）躁狂发作;（5）妄想;（6）发作时的精神病特征;（7）烟草成瘾;（8）BD家族史;（9）幻觉;和（10）共病焦虑症，（11）酒精依赖，（12）饮食失调和（13）物质依赖。局限性：由于样本量小，该研究受到限制，仅纳入自我报告和临床医生观察的临床变量及其横断面设计。讨论：结果表明，ML方法可以有效地确定患者的PP。此外，虽然以前没有报道，一些变量，如烟草使用和共病饮食失调，似乎与PP密切相关。
Background: Bipolar disorder (BD) is a severe psychiatric disorder characterized by periodic episodes of manic and depressive symptomatology. Predominant polarity (PP) appears to be an important specifier of BD. The present study employed machine learning (ML) algorithms to accurately determine a patient ' s PP without the inclusion of number and polarity of past episodes, while exploring associations between PP and demographic and clinical variables.Methods: From a cohort of 148 BD patients, demographic and clinical variables were collected using a customized questionnaire and the SCID-CV. The algorithm employed was the Random-Forest method. The algorithm was programed to classify patients into either depressive or manic predominant polarities and to reveal which variables were associated to the specifier.Results: The algorithm attained an AUC ROC of 74.72% (95% CI = 72.29-77.15%) in classifying patients into either manic or depressive PP. The variables selected by the algorithm were: (1) age at first depressive episode; (2) number of hospitalizations; (3) BD Type II; (4) manic onset; (5) delusions; (6) psychotic features at onset; (7) tobacco addiction; (8) family history of BD; (9) hallucinations; and (10) comorbid anxiety disorders, (11) alcohol dependence, (12) eating disorders and (13) substance dependence.Limitations: The study is limited due to the small sample size, the inclusion of only self-reported and clinician-observed clinical variables and its cross-sectional design.Discussion: The results suggest that the ML approach could be effective in determining a patient ' s PP. Furthermore, although not previously reported, some variables, such as tobacco use and comorbid eating disorders, appear to be closely associated with PP.