Personalized Longevity Healthcare Through Artificial Intelligence and Predictive Modeling for Aging Populations

Abstract

Population aging across the world is gaining momentum, whereby people aged 60 years and above will make up 2.1 billion, which is 22% of the total global population. Population aging results in the increased burden of chronic diseases, multimorbidity, and increased healthcare expenditure. Traditional “fit-for-all” models used in delivering geriatric care lack the capability of delivering proactive and personalized medical interventions. This research introduces a personalized longevity care system using AI, which will use data from electronic clinical records, sensors, lifestyle habits, and biomarkers. The proposed model uses deep learning for time-series predictions, clustering for patient segmentation and risk stratification, and SHAP (explanatory AI). Synthetic cohort study involving 5,000 elderly people was conducted using synthetic simulation of demographic and epidemiologic distributions to assess model performance. According to the results, the presented approach demonstrated 91.3% prediction accuracy of early onset of chronic disease, increased the accuracy of risk stratification by 27% as compared to conventional statistical approaches, and helped reduce preventable hospitalization by 32% due to early warnings. The personalized health care plans contributed to increasing the adherence to the prescribed medication and lifestyle changes by 25%. Therefore, the suggested framework may allow transitioning from treatment to proactive health care management, which will help use resources more efficiently and improve patients' quality of life. Through integration of medicine, data science, and public health, the presented model corresponds with the principles of lifespan innovation.