PDF(Pubmed)
Abstract：
UNASSIGNED: Social anxiety disorder (SAD) is characterized by heightened sensitivity to social interactions or settings, which disrupts daily activities and social relationships. This study aimed to explore the feasibility of utilizing digital phenotypes for predicting the severity of these symptoms and to elucidate how the main predictive digital phenotypes differed depending on the symptom severity.
UNASSIGNED: We collected 511 behavioral and physiological data over 7 to 13 weeks from 27 SAD and 31 healthy individuals using smartphones and smartbands, from which we extracted 76 digital phenotype features. To reduce data dimensionality, we employed an autoencoder, an unsupervised machine learning model that transformed these features into low-dimensional latent representations. Symptom severity was assessed with three social anxiety-specific and nine additional psychological scales. For each symptom, we developed individual classifiers to predict the severity and applied integrated gradients to identify critical predictive features.
UNASSIGNED: Classifiers targeting social anxiety symptoms outperformed baseline accuracy, achieving mean accuracy and F1 scores of 87% (with both metrics in the range 84-90%). For secondary psychological symptoms, classifiers demonstrated mean accuracy and F1 scores of 85%. Application of integrated gradients revealed key digital phenotypes with substantial influence on the predictive models, differentiated by symptom types and levels of severity.
UNASSIGNED: Leveraging digital phenotypes through feature representation learning could effectively classify symptom severities in SAD. It identifies distinct digital phenotypes associated with the cognitive, emotional, and behavioral dimensions of SAD, thereby advancing the understanding of SAD. These findings underscore the potential utility of digital phenotypes in informing clinical management.
UNASSIGNED: We collected 511 behavioral and physiological data over 7 to 13 weeks from 27 SAD and 31 healthy individuals using smartphones and smartbands, from which we extracted 76 digital phenotype features. To reduce data dimensionality, we employed an autoencoder, an unsupervised machine learning model that transformed these features into low-dimensional latent representations. Symptom severity was assessed with three social anxiety-specific and nine additional psychological scales. For each symptom, we developed individual classifiers to predict the severity and applied integrated gradients to identify critical predictive features.
UNASSIGNED: Classifiers targeting social anxiety symptoms outperformed baseline accuracy, achieving mean accuracy and F1 scores of 87% (with both metrics in the range 84-90%). For secondary psychological symptoms, classifiers demonstrated mean accuracy and F1 scores of 85%. Application of integrated gradients revealed key digital phenotypes with substantial influence on the predictive models, differentiated by symptom types and levels of severity.
UNASSIGNED: Leveraging digital phenotypes through feature representation learning could effectively classify symptom severities in SAD. It identifies distinct digital phenotypes associated with the cognitive, emotional, and behavioral dimensions of SAD, thereby advancing the understanding of SAD. These findings underscore the potential utility of digital phenotypes in informing clinical management.
摘要：
■社交焦虑症（SAD）的特征是对社交互动或环境的敏感性增强，破坏日常活动和社会关系。本研究旨在探索利用数字表型预测这些症状严重程度的可行性，并阐明主要预测数字表型如何根据症状严重程度而有所不同。
■我们在7至13周内从27名SAD和31名健康个体使用智能手机和智能手环收集了511份行为和生理数据，从中提取了76个数字表型特征。为了减少数据维度，我们使用了一个自动编码器，将这些特征转化为低维潜在表示的无监督机器学习模型。用三种社交焦虑特异性量表和九种其他心理量表评估症状严重程度。对于每个症状，我们开发了单个分类器来预测严重程度,并应用综合梯度来识别关键预测特征.
■针对社交焦虑症状的分类器优于基线准确性，平均准确率和F1评分为87%(两个指标都在84-90%范围内)。对于继发性心理症状，分类器的平均准确率和F1评分为85%.整合梯度的应用揭示了对预测模型有重大影响的关键数字表型，根据症状类型和严重程度进行区分。
■通过特征表征学习利用数字表型可以有效地对SAD中的症状严重性进行分类。它确定了与认知相关的不同数字表型，情感,和SAD的行为维度，从而推进对SAD的理解。这些发现强调了数字表型在指导临床管理方面的潜在效用。
■我们在7至13周内从27名SAD和31名健康个体使用智能手机和智能手环收集了511份行为和生理数据，从中提取了76个数字表型特征。为了减少数据维度，我们使用了一个自动编码器，将这些特征转化为低维潜在表示的无监督机器学习模型。用三种社交焦虑特异性量表和九种其他心理量表评估症状严重程度。对于每个症状，我们开发了单个分类器来预测严重程度,并应用综合梯度来识别关键预测特征.
■针对社交焦虑症状的分类器优于基线准确性，平均准确率和F1评分为87%(两个指标都在84-90%范围内)。对于继发性心理症状，分类器的平均准确率和F1评分为85%.整合梯度的应用揭示了对预测模型有重大影响的关键数字表型，根据症状类型和严重程度进行区分。
■通过特征表征学习利用数字表型可以有效地对SAD中的症状严重性进行分类。它确定了与认知相关的不同数字表型，情感,和SAD的行为维度，从而推进对SAD的理解。这些发现强调了数字表型在指导临床管理方面的潜在效用。
