Machine learning prediction algorithms for 2- , 5- and 10-year risk of Alzheimer's, Parkinson's and dementia at age 65: a study using medical records from France and the UK General Practitioners

• Published in: bioRxiv (Cold Spring Harbor Laboratory)
• Main Authors: Nedelec, Thomas, Zaidi, Karim, Montaud, Charlotte, Guinebretiere, Octave, Sipilä, Pyry, Wei, Dang, Yang, Fen, Freydenzon, Anna, Belloir, Antoine, Fournier, Nemo, Hamieh, Nadine, Lekens, Beranger, Slaouti, Yanis, McRae, Allan, Couvy-Duchesne, Baptiste, Hswen, Yulin, Fang, Fang, Kivimäki, Mika, Ansart, Manon, Durrleman, Stanley
• Format: Journal Article
• Language: English
• Published: United States 25.01.2025
• DOI: 10.1101/2025.01.22.25320969

Leveraging machine learning on electronic health records offers a promising method for early identification of individuals at risk for dementia and neurodegenerative diseases. Current risk algorithms heavily rely on age, highlighting the need for alternative models with strong predictive power, especially at age 65, a crucial time for early screening and prevention. This prospective study analyzed electronic health records (EHR) from 76,427 adults (age 65, 52.1% women) using the THIN database. A general risk algorithm for Alzheimer's disease, Parkinson's disease, and dementia was developed using machine learning to select predictors from diagnoses, and medications. Medications (e.g., laxatives, urological drugs, antidepressants), along with sex, BMI, and comorbidities, were key predictors. The algorithm achieved a 38.4% detection rate at a 5% false-positive rate for 2-year dementia prediction. The validated prediction algorithms, easy to implement in primary care, identify high-risk 65-year-olds using medication records. Further refinement and broader validation are needed.