An End-to-End Machine Learning Framework for Predicting Common Geriatric Diseases

Interdisciplinary applications between information technology and geriatrics have been accelerated in recent years by the advancement of artificial intelligence,cloud computing,and 5G technology,among others.Meanwhile,applications developed by using the above technologies make it possible to predict the risk of age-related diseases early,which can give caregivers time to intervene and reduce the risk,potentially improving the health span of the elderly.However,the popularity of these applications is still limited for several reasons.For example,many older people are unable or unwilling to use mobile applications or devices(e.g.smartphones)because they are relatively complex operations or time-consuming for older people.In this work,we design and implement an end-to-end framework and integrate it with the WeChat platform to make it easily accessible to elders.In this work,multifactorial geriatric assessment data can be collected.Then,stacked machine learning models are trained to assess and predict the incidence of common diseases in the elderly.Experimental results show that our framework can not only provide more accurate prediction(precision:0.8713,recall:0.8212)for several common elderly diseases,but also very low timeconsuming(28.6 s)within a workflow compared to some existing similar applications.

Epigenetic Signatures of Aging: A Comprehensive Study of Biomarker Discovery

Background: Aging is a complex biological process that is associated with a decline in physiological functions and an increased risk of age-related diseases. Despite advances in molecular biology and genetics, the underlying mechanisms of aging remain largely unknown. Study: The identification of biomarkers of aging would provide a powerful tool for monitoring the effects of aging and for developing interventions to improve healthspan. Aging is associated with alterations in genetics, epigenetic marks, telomere shortening, cell senescence, and changes in the expression of genes involved in metabolism, inflammation, and DNA damage repair. Epigenetic changes, including modifications to DNA methylation and histone acetylation patterns, play a critical role in the aging process. As we age, these changes can lead to altered gene expression and contribute to the development of age-related diseases such as cancer, Alzheimer’s disease (AD) and cardiovascular disease (CVD). Conclusion: The discovery of aging biomarkers that are sensitive to these epigenetic changes has the potential to revolutionize our understanding of the aging process and inform the development of interventions to improve healthspan and extend lifespan.