Research led by Harvard University shows an artificial intelligence algorithm can predict how likely it is that a given patient will develop 348 diseases based on data collected from electronic health records (EHRs) and genetic data.
As reported in Nature, the researchers built a new computational tool called ALADYNOULLI, a statistical machine learning model known as a Bayesian generative model, that jointly analyzes EHR data and genetics to model how disease risk evolves over an individual's lifetime.
They applied it to over 683,000 participants across three independent biobanks, the UK Biobank, Mass General Brigham, and All of Us, covering 348 diseases and up to 52 years of follow-up.
ALADYNOULLI identified 21 disease signatures, which the researchers defined as clusters of conditions that tend to co-occur and evolve together over time, such as cardiovascular or metabolic disease.
These were remarkably consistent across all three biobanks.
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