A group of researchers from the European Bioinformatics Institute, the German Cancer Research Center (DKFZ), and several Danish institutions propose applying the same technology behind large language models — like ChatGPT — to learn and predict the natural history of more than a thousand diseases simultaneously.
The resulting model, named Delphi-2M, can identify disease patterns from medical histories, lifestyle factors, and preexisting health conditions.
The algorithm was trained on data from 400,000 individuals in the United Kingdom and validated using records from nearly two million patients in Denmark.
It can project health trajectories — both at the population and individual level — for up to two decades.
Rather than predicting exactly what will happen to a specific individual at a given moment, it calculates the likelihood of developing certain diseases over a defined period.
Delphi-2M achieves accuracy comparable to the best disease-specific models for conditions such as dementia or myocardial infarction and outperforms existing mortality prediction algorithms.
The study also identified diseases that increase the risk of others, such as mental disorders or certain female reproductive system tumors.
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