ABSTRACT
Given the globally ageing population, governments need to prepare for the challenges of providing good-quality health care while supporting the independence and quality-of-life needs of older people. The complexity of this challenge requires novel and cross-disciplinary solutions. Current research in the area of gerontology has not fully recognized the benefits of artificial intelligence (AI)–based solutions to addressing these issues. AI and mathematical modeling techniques present the opportunity to better conceptualize and develop new insights in the area of gerontology, with the ultimate goal of designing, testing, and implementing interventions that can improve the quality of life of older people and address the challenges of an ageing population. This chapter provides an insight into five key AI and mathematical modeling techniques, namely Bayesian network, compartmental model, multiagent system, fuzzy logic, and fuzzy cognitive maps. The fundamentals of the techniques are elaborated with example scenarios chosen from long-term care (LTC) institutions. The chapter critically evaluates each of the techniques by demonstrating the strengths and weaknesses of the various approaches.
