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Title:Learning a long healthy aging
Author(s):Faghri, Faraz
Director of Research:Campbell, Roy H.
Doctoral Committee Chair(s):Campbell, Roy H.
Doctoral Committee Member(s):Zhai, ChengXiang; Peng, Jian; Li, King; Singleton, Andrew B; Nalls, Mike A
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Computer Science
Artificial Intelligence
Machine Learning
Parkinson's disease
Alzheimer's disease
Amyotrophic Lateral Sclerosis (ALS)
Intensive Care Unit (ICU)
Abstract:The human body is formed from the DNA code within a person's zygote. As the body is programmatically built from the zygote, the swarms of RNA/DNA greatly increase, eventually forming the neurological system and kick-starting it to become intelligent. The human body is gradually but continuously changing; we call this gradual accumulation of changes, aging. We age at different rates, in different forms, depending on many factors throughout our lifespan. Ultimately, the many physical systems that make up our body begin to fail at the same time and in mutually detrimental ways. The human body is a machine, and like any machine, it can be modeled, predicted, and maintained for a substantial length of time. We may postpone or reduce the undesired effects of aging. Maintaining physical and mental health, avoiding disorders, and remaining active and independent. Aging is part of the human experience, and we can strive to make it positive. This forms the basis for considering wellbeing in older age, healthy aging. Quantifying the human body as a machine can illuminate what are the elements of a healthy aging process and avoid undesirable outcomes. We may predict the aging trajectory, the rate and form of changes, the occurrence of degenerative disorders such as Alzheimer's, Parkinson's, and Amyotrophic lateral sclerosis (ALS). We may prescribe lifestyle changes. We may intervene and prevent an undesirable trajectory. In pursuit of healthy aging, we utilize data-driven approaches to learn and model the aging of the human body. We utilize the power of data analytics, machine learning, cloud computing, and well-curated datasets. We use machine learning techniques on longitudinal data to develop descriptive, predictive, and prescriptive models of aging. We focus on aging and neurological disorders as one of the most prominent health disorders in our aging population. Our solutions impact the whole spectrum of healthcare from patients and caregivers to physicians and clinicians to providers and insurers.
Issue Date:2019-12-06
Rights Information:Copyright 2019 Faraz Faghri
Date Available in IDEALS:2020-03-02
Date Deposited:2019-12

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