AI accelerates personalized medicine by providing a patient’s risk of developing two or more disease diagnoses decades in advance by identifying disease trajectories.
machine learning has the potential to accelerate personalized medicine by providing a patient’s risk of developing two or more disease diagnoses decades in advance by identifying disease trajectories.
The study used a massive dataset of 44 million records of hospital stays from 2003 to 2014, selected from an electronic health registry database consisting of 8.9 million patients, nearly the entire population, from all hospitals in Austria from 1997 through 2014. The researchers decided to use an unsupervised AI clustering algorithm for this study. In data science, clustering is a method of dividing an unlabeled dataset into subgroups of related data. Unsupervised machine learning finds meaningful data groupings and patterns without human interference by analyzing and clustering unlabeled datasets.
“The disease trajectory framework can help us to identify critical events as specific combinations of risk factors that put patients at high risk for different diagnoses decades later,” the scientists wrote.
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