Catalyst Awardee

Project Description

AI and Healthy Ageing: Developing AI-based Computational Framework and Approaches to Define Healthy Ageing in the Singapore Longitudinal Ageing Study (SLAS) Cohort

Zhong Xin, PhD; Yang Feng, PhD; NG Tze Pin, MBBS, PhD, FAFOEM, FAMS, MFPHM
Competition Sponsor: Ministry of Health and National Research Foundation of Singapore
Awardee year: 2021

The improvements of the health care systems, wealth, and major advances in medical sciences leads to extended human lifespan. The current challenge is the increasing number of elderly visiting healthcare institutions. Older adults are expected to prolong their healthspan and maintain the productivity. As 70% of the older population is living in the community, we are greatly promoting ageing-in-place in Singapore. However, poor understanding of healthy ageing may plague efficient health program/service designs. We aim to develop AI-based computational framework and approaches to help formulate novel ways of defining healthy ageing in the Singapore Longitudinal Ageing Study (SLAS) Cohort. Our hypothesis is that healthy elderly will display specific determinants from non-healthy agers and that gradation of determinants will follow the progression of age-related conditions. Our data already possesses more than 10 years longitudinal samples from elderly Singaporean (n>3000; 55-94 years), including nutritional, socio-economic, cognitive, physical, medical history, lifestyle, activities, medication, hospitalization, comorbidities, frailty, mortality which would allow the most meaningful study of Asian elderly. In contrast to traditional theory-based and hypotheses-driven approaches, the AI-based approaches are innovative by selecting markers and predicting health outcomes without any prior assumptions in a train-test manner to formulate novel phenotypes of healthy ageing.

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