Leveraging Advanced AI Algorithms to Revolutionize Health Monitoring for Seniors: a Comprehensive Analysis of Data from Wearables, EHRs, and Beyond

EasyChair Preprint no. 14103

Abstract

The world’s population is experiencing unprecedented growth in its elderly segment, leading to increased demand for accessible, ef- ficient, and effective health monitoring solutions catered explicitly to seniors’ needs. Traditional health monitoring strategies often rely on pe- riodic assessments conducted at clinics or hospitals, which may overlook subtle changes in individuals’ physiological status between visits. Conse- quently, early detection of age-related declines or diseases becomes chal- lenging, potentially resulting in delayed intervention and adverse conse- quences for patients.

As the global population ages, there is a growing need for advanced health monitoring systems tailored to seniors’ unique requirements. These systems must be able to accurately detect early signs of physical de- cline or illness and provide timely interventions to ensure optimal health and wellbeing. Fortunately, recent advances in artificial intelligence (AI), particularly in algorithmic design, offer exciting opportunities for revolu- tionizing health monitoring in older adults. Drawing upon diverse data streams from wearable devices, electronic health records (EHRs), and other relevant sources, sophisticated AI models hold tremendous promise in enhancing health surveillance, promoting preventive care, and ulti- mately improving quality of life among seniors. This paper presents a comprehensive review of state-of-the-art AI algorithms applied to health monitoring for older adults, with a specific focus on wearables, EHRs, and beyond. We summarize the latest findings, outline critical challenges, and propose future research directions to maximize the benefits of these emerging tools for our aging society.

Keyphrases: Artificial Intelligence, deep learning, Electronic Health Records, health monitoring, machine learning, older adults, preventive care, quality of life, Senior Populations, Wearables

@Booklet{EasyChair:14103,
  author = {Navjot Singh and Shanu Khare and Payal Thakur and Karan Sarawagi},
  title = {Leveraging Advanced AI Algorithms to Revolutionize Health Monitoring for Seniors: a Comprehensive Analysis of Data from Wearables, EHRs, and Beyond},
  howpublished = {EasyChair Preprint no. 14103},

  year = {EasyChair, 2024}}