可穿戴计算机
计算机科学
人工智能
机器学习
心情
可穿戴技术
医学诊断
精神分裂症(面向对象编程)
医学
精神科
嵌入式系统
病理
程序设计语言
作者
Duc-Khanh Nguyen,Chien‐Lung Chan,Ai‐Hsien Li,Dinh‐Van Phan,Chung-Hsien Lan
标识
DOI:10.1177/14604582221137537
摘要
In the modern world, with so much inherent stress, mental health disorders (MHDs) are becoming more common in every country around the globe, causing a significant burden on society and patients’ families. MHDs come in many forms with various severities of symptoms and differing periods of suffering, and as a result it is difficult to differentiate between them and simple to confuse them with each other. Therefore, we propose a support system that employs deep learning (DL) with wearable device data to provide physicians with an objective reference resource by which to make differential diagnoses and plan treatment. We conducted experiments on open datasets containing activity motion signal data from wearable devices to identify schizophrenia and mood disorders (bipolar and unipolar), the datasets being named Psykose and Depresjon. The results showed that, in both workflow approaches, the proposed framework performed well in comparison with the traditional machine learning (ML) and DL methods. We concluded that applying DL models using activity motion signal data from wearable devices represents a prospective objective support system for MHD differentiation with a good performance.
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