This project advances national health and promotes science and technology development by providing algorithms, software, and systems that can train machine learning models on electronic health records (EHRs) for accurate and early prediction of Alzheimer’s Disease and Related Dementias (ADRD). ADRD is a severe neurodegenerative disorder that effects over 5,000,000 people over the age of 65 that is characterized by progressive memory, cognitive impairment and personality changes, which can further evolve to dementia and death. Early prediction of ADRD is crucial for timely intervention and improved patient outcomes. Recent studies have shown that personal risk factors such as education, employment, and lifestyle or family history significantly influence ADRD onset and progression. However, these factors are not recorded in a structured format within the existing EHRs. In contrast, personal risk factors are often embedded within the free text of clinical notes or discharge summaries that are not easily searchable, computable, or standardized. This creates a major technical barrier for their integration into the ADRD prediction models. To address this, this project develops a computational platform using novel machine learning and natural language processing to automatically extract personal risk factors from EHR clinical narratives and leverage them for accurate and early prediction of ADRD. This research significantly improves ADRD prediction accuracy and timeliness, with pote