内容紹介

Digital phenotyping and machine learning technologies have shown a potential to measure objective behavioral and physiological markers, provide risk assessment for people who might have a high risk of poor health and wellbeing, and help make better decisions or behavioral changes to support health and wellbeing. I will introduce a series of studies, algorithms, and systems we have developed for measuring, predicting, and supporting personalized health and wellbeing. I will also discuss challenges, learned lessons, and potential future directions in health and wellbeing research.

詳細情報

刊年

2022

形態

電子化映像資料(1時間25分23秒）

シリーズ名

情報科学領域・コロキアム ; 2022年度

注記

講演者所属: Rice University

講演日: 2022年10月18日 2限

講演場所: 情報科学棟 エーアイ大講義室(L1)

標題言語

英語 (eng)

本文言語

英語 (eng)

著者情報

Sano, Akane