행사/교육
Developing time-series machine learning methods to unlock new insights from large-scale biomedical resources – Aiden Doherty
- 등록일2025-09-29
- 조회수93
- 구분 국내
- 행사교육분류 행사
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주관기관
기초과학연구원
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행사장소
Online 개최
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행사기간
2025-10-15
- 원문링크
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키워드
#대규모 생물의학 자원 #biomedical #시계열 머신 러닝 #time-series machine
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첨부파일
Biomedical Mathematics Online Colloquium
Developing time-series machine learning methods to unlock new insights from large-scale biomedical resources – Aiden Doherty
대규모 생물의학 자원에서 새로운 통찰력을 얻기 위한 시계열 머신 러닝 방법 개발 – Aiden Doherty
October 15 @ 4:00 pm - 5:00 pm KST
ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: 1234) + Google Map
https://www.ibs.re.kr/bimag/event/developing-time-series-machine-learning-methods-to-unlock-new-insights-from-large-scale-biomedical-resources-aiden-doherty/
Speaker
Aiden Doherty
Big Data Institute, University of Oxford
Abstract
Smartphones and wearable devices provide a major opportunity to transform our understanding of the mechanisms, determinants, and consequences of diseases. For example, around 9 in 10 people own a smartphone in the United Kingdom, while one-fifth of US adults own wearable technologies. This high level of device ownership means that many people could contribute to health research from the comfort of their home by offering small amounts of time to share data and help address health-related questions that matter to them. A leading example is the seven day wrist-worn accelerometer data measured in 100,000 UK Biobank participants between 2013-2015 that has led to important new findings. These include discoveries of: new genetic variants for sleep and activity; small amounts of vigorous non-exercise physical activity being associated with substantially lower mortality; and no apparent upper threshold to the benefits of physical activity with respect to cardiovascular disease risk. However, challenges exist around cost, access, validity, and training. In this talk I will review progress made in this exciting new area of health data science and share opportunities for self-supervised time-series machine learning to provide new insights into physical activity, sleep, heart rhythms and other exposures relevant to health and disease.
Details
Date:
October 15
Time:
4:00 pm - 5:00 pm KST
Event Category:
Biomedical Mathematics Online Colloquium
Organizer
Jae Kyoung Kim
jaekkim@kaist.ac.kr
...................(계속)
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