Using machine learning analyses of speech to classify levels of expressed emotion in parents of youth with mood disorders.
Using machine learning analyses of speech to classify levels of expressed emotion in parents of youth with mood disorders.
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DOI：
10.1016/j.jpsychires.2021.01.019
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发表时间：
2021-04
影响因子：
4.8
通讯作者：
Miklowitz DJ
中科院分区：
文献类型：
--
作者：
Weintraub MJ;Posta F;Arevian AC;Miklowitz DJ
情绪表达（EE）是衡量照顾者对精神疾病患者态度的一项指标，是情绪和精神疾病复发的有力预测指标。由于EE的测量是时间密集型和昂贵的，其在临床环境中的使用受到限制。为了自动化EE分类，我们评估了应用于语音样本词汇特征的机器学习（ML）是否可以准确地将父母分类为EE或其亚型（批评，过度参与和温暖）的高或低。样本是123名有活跃情绪症状和双相情感障碍家族史的年轻人的父母。使用ML算法，我们在将父母分类为EE和EE亚型的高或低方面实现了75.2-81.8%的准确性（灵敏度为~0.7，特异性为~0.8）。此外，机器导出的EE分类与情绪症状、父母痛苦和家庭冲突的关系超出了EE分类与相同变量的关系。值得注意的是，批评与更严重的躁狂症，父母的痛苦和家庭冲突有关。研究结果表明，EE分类可以通过词法分析自动化，并建议在临床环境中促进大规模应用的潜力。这些结果还提供了EE及其亚型的数字表型的初步迹象。
Expressed emotion (EE), a measure of attitudes among caregivers towards a patient with a psychiatric disorder, is a robust predictor of relapse across mood and psychotic disorders. Because the measurement of EE is time-intensive and costly, its use in clinical settings has been limited. In an effort to automate EE classification, we evaluated whether machine learning (ML) applied to lexical features of speech samples can accurately categorize parents as high or low in EE or in its subtypes (criticism, overinvolvement, and warmth). The sample was 123 parents of youth who had active mood symptoms and a family history of bipolar disorder. Using ML algorithms, we achieved 75.2–81.8% accuracy (sensitivities of ~0.7 and specificities of ~0.8) in classifying parents as high or low in EE and EE subtypes. Further, machine-derived EE classifications’ relationships with mood symptoms, parental distress, and family conflict paralleled observer-rated EE classifications’ relationships with the same variables. Of note, criticism related to greater manic severity, parental distress, and family conflict. Study findings indicate that EE classification can be automated through lexical analysis and suggest potential for facilitating larger-scale applications in clinical settings. The results also provide initial indications of the digital phenotypes that underlie EE and its subtypes.
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影响因子：
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通讯作者：
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