Background: 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. Methods: 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. Results: 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. Conclusion: 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.

YS, and BE are full time employees of Cegedim. All other authors declare no competing interests.

The research leading to these results has received funding from the joint program in neurodegenerative diseases (JPND) ANR-21-JPW2-0002-01 (LeMeReND) and the program 'Investissements d'avenir' ANR-10-IAIHU-06. This work was funded in part by the French government under management of Agence Nationale de la Recherche as part of the "Investissements d'avenir" program, reference ANR-19- P3IA-0001 (PRAIRIE 3IA Institute). PNS was supported by the Emil Aaltonen Foundation and the Finnish Medical Foundation. MK was supported by Wellcome Trust, UK (221854/Z/20/Z), National Institute on Aging (NIH), US (R01AG056477), Medical Research Council, UK (MR/R024227/1, MR/Y014154/1) and Academy of Finland (350426). BCD is supported by Inria, and a NHMRC CJ Martin fellowship (1161356).

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethical approval was obtained before carrying out this study (and for the use of datasets). The study was a retrospective analysis of secondary pseudo-anonymised patient data only and do not involve direct patient intervention. The study was approved by the THIN Scientific Research Committee (SRC; SRC reference 22-008-R2). There was no need for written informed consent from participants. No photographs, videos, or other information of recognizable persons is used in this article. Authorization for disclosure was, therefore, not necessary. For the UK database, data are only available to researchers carrying out approved medical research. Ethical approval was granted by the NHS South-East Multicentre Research Ethics Committee in 2003 (reference 03/01/073) for the establishment of the THIN database, and was updated in 2011. A further update was carried out and approval was granted in 2020 by the NHS South Central, Oxford C Research Ethics Committee (reference 20/SC/0011). For the French database, several audits were done by the Commission Nationale de l'Informatique et des Libertes, the French authority responsible for the protection of personal data. Data are available from GERS SAS for researchers who meet the criteria for access to confidential data.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

The data used in the preparation of this Article are available from the Cegedim company upon reasonable request (info@the-health- improvement-network.co.uk).