Smart Prediction Models for Alzheimer's Care
Keywords:
Artificial intelligence, Parkinson’s disease, predictive analytics, risk stratification, healthcare systems, formalized care guidance, early diagnosis.Abstract
Aging is the most significant independent risk factor for Alzheimer’s disease (AD). Consequently, aging populations are witnessing increased AD incidence, elevating futures of cognitive decline in a growing segment of society. Predictive analytic tools are emerging to support earlier diagnosis, targeting therapeutics when they have the highest efficacy potential, and improved management, easing the care burden on both family and healthcare systems. Advances in AI/machine learning improve prediction accuracy, offering risk scores and other insights to inform practice, allocate care resources efficiently, and guide the creation of personalized treatment plans. At the same time, patient-generated health data are accumulating rapidly, enhancing screening, clinical trial recruitment, and longitudinal monitoring.
Automated Natural Language Processing (NLP) methods applied to electronic health record (EHR) data can also improve prediction prevalence and granularity. Growing EHR volume combined with augmented risk modelling enables development of predictive models for rarer conditions such as frontotemporal dementia and Alzheimer’s disease with frontal lobe involvement. Cognitive decline, dementia, or Alzheimer’s disease diagnosis within 3 years can be subjected to risk scoring. NLP can predict correspondingly non-Alzheimer’s disease dementia, vascular dementia, or frontotemporal dementia diagnosis on a similar timeframe.
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