Evaluating depression with multimodal wristband-type wearable device: screening and assessing patient severity utilizing machine-learning

Evaluating depression with multimodal wristband-type wearable device: screening and assessing patient severity utilizing machine-learning

Objective: We aimed to develop a machine learning algorithm to screen for depression and assess severity based on data from wearable devices. Methods: We used a wearable device that calculates steps, energy expenditure, body movement, sleep time, heart rate, skin temperature, and ultraviolet light e...

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Bibliographic Details
Main Authors: Yuuki Tazawa, Kuo-ching Liang, Michitaka Yoshimura, Momoko Kitazawa, Yuriko Kaise, Akihiro Takamiya, Aiko Kishi, Toshiro Horigome, Yasue Mitsukura, Masaru Mimura, Taishiro Kishimoto
Format: Article
Language:English
Published: Elsevier 2020-02-01
Series:Heliyon
Subjects:
Psychiatry
Biological psychiatry
Depression
Clinical research
Health informatics
Health technology
Online Access:http://www.sciencedirect.com/science/article/pii/S2405844020301195

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http://www.sciencedirect.com/science/article/pii/S2405844020301195

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