Choi Hyoungshin, Cho Yesol, Min Choongki, Kim Kyungnam, Kim Eunji, Lee Seungmin, Kim Jae-Jin
AI Medtech R&D, Waycen Inc, Seoul, Republic of Korea.
Department of Electrical and Computer Engineering, Sungkyunkwan University and Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea.
Digit Health. 2024 May 22;10:20552076241256730. doi: 10.1177/20552076241256730. eCollection 2024 Jan-Dec.
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.
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.
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.
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.
社交焦虑障碍（SAD）的特征是对社交互动或场景高度敏感，这会扰乱日常活动和社会关系。本研究旨在探讨利用数字表型预测这些症状严重程度的可行性，并阐明主要的预测性数字表型如何因症状严重程度而异。
我们使用智能手机和智能手环，在7至13周内收集了27名社交焦虑障碍患者和31名健康个体的511条行为和生理数据，从中提取了76个数字表型特征。为了降低数据维度，我们采用了自动编码器，这是一种无监督机器学习模型，可将这些特征转换为低维潜在表示。使用三个社交焦虑特异性量表和另外九个心理量表评估症状严重程度。对于每种症状，我们开发了个体分类器来预测严重程度，并应用积分梯度来识别关键的预测特征。
针对社交焦虑症状的分类器优于基线准确率，平均准确率和F1分数达到87%（两个指标范围均为84-90%）。对于继发性心理症状，分类器的平均准确率和F1分数为85%。积分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，这些表型因症状类型和严重程度而异。
通过特征表示学习利用数字表型可以有效地对社交焦虑障碍的症状严重程度进行分类。它识别出与社交焦虑障碍的认知、情感和行为维度相关的不同数字表型，从而增进了对社交焦虑障碍的理解。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。
