Our study demonstrates how clinical data can be used to build machine-learning models to predict the risk of Alzheimer’s disease (AD) onset and can be integrated with knowledge networks to gain insights into the pathophysiology of AD, with a focus on a better understanding of disease sex differences.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
2023 Alzheimer’s disease facts and figures. Alzheimers Dement. 19, 1598–1695 (2023). Yearly comprehensive summary of the latest clinical information about AD, including diagnosis and treatment.
Ferrari, C. & Sorbi, S. The complexity of Alzheimer’s disease: an evolving puzzle. Physiol. Rev. 101, 1047–1081 (2021). A review that covers understanding of the heterogeneity in AD identification, manifestation and pathogenesis.
Ferretti, M. T. et al. Sex differences in Alzheimer disease — the gateway to precision medicine. Nat. Rev. Neurol. 14, 457–469 (2018). A review that presents sex differences in AD presentation.
Tang, A. S. et al. Deep phenotyping of Alzheimer’s disease leveraging electronic medical records identifies sex-specific clinical associations. Nat. Commun. 13, 675 (2022). An example of disease- and sex-specific phenotyping with clinical data by exploring comorbidities.
Morris, J. H. et al. The scalable precision medicine open knowledge engine (SPOKE): a massive knowledge graph of biomedical information. Bioinformatics 39, btad080 (2023). A paper that introduces the SPOKE biomedical knowledge network.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This is a summary of: Tang, A. S. et al. Leveraging electronic health records and knowledge networks for Alzheimer’s disease prediction and sex-specific biological insights. Nat. Aging https://doi.org/10.1038/s43587-024-00573-8 (2024).
Rights and permissions
About this article
Cite this article
Disease insights from medical data using interpretable risk prediction models. Nat Aging 4, 293–294 (2024). https://doi.org/10.1038/s43587-024-00585-4
Published:
Issue Date:
DOI: https://doi.org/10.1038/s43587-024-00585-4
