| Title: | Medical history predicts phenome-wide disease onset and enables the rapid response to emerging health threats |
| Journal: | Nature Communications |
| Published: | 20 May 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38763986/ |
| DOI: | https://doi.org/10.1038/s41467-024-48568-8 |
| URL: | https://www.nature.com/articles/s41467-024-48568-8.pdf |
Publication 12020
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Abstract
The COVID-19 pandemic exposed a global deficiency of systematic, data-driven guidance to identify high-risk individuals. Here, we illustrate the utility of routinely recorded medical history to predict the risk for 1883 diseases across clinical specialties and support the rapid response to emerging health threats such as COVID-19. We developed a neural network to learn from health records of 502,460 UK Biobank. Importantly, we observed discriminative improvements over basic demographic predictors for 1774 (94.3%) endpoints. After transferring the unmodified risk models to the All of US cohort, we replicated these improvements for 1347 (89.8%) of 1500 investigated endpoints, demonstrating generalizability across healthcare systems and historically underrepresented groups. Ultimately, we showed how this approach could have been used to identify individuals vulnerable to severe COVID-19. Our study demonstrates the potential of medical history to support guidance for emerging pandemics by systematically estimating risk for thousands of diseases at once at minimal cost.</p>
13 Authors
- Jakob Steinfeldt
- Benjamin Wild
- Thore Buergel
- Maik Pietzner
- Julius Upmeier zu Belzen
- Andre Vauvelle
- Stefan Hegselmann
- Spiros Denaxas
- Harry Hemingway
- Claudia Langenberg
- Ulf Landmesser
- John Deanfield
- Roland Eils
1 Application
| Application ID | Title |
|---|---|
| 51157 | Stratification and modelling of competing risks in cardiovascular disease events based on multi-level patient characteristics and genetics |
Enabling scientific discoveries that improve human health