With huge patient dataset, AI accurately predicts treatment outcomes

The model was front-loaded with de-identified data on millions of patients gleaned from health care claims information submitted by employers, health plans and hospitals -- a foundation model strategy similar to that of generative AI tools like ChatGPT.

Replacing gold standard clinical research is not the point -- but researchers hope machine learning could help save time and money by putting clinical trials on a faster track and support the personalization of patient care. Unlabeled data used to pre-train the model came from MarketScan Commercial Claims and Encounters from 2012-2017, providing 3 million patient cases, 9,435 medical codes and 9,153 medication codes.

As part of comparing the model to other machine learning tools and validating it against clinical trial results, the study showed that the broad pre-training is the backbone of CURE's effectiveness -- and incorporation of knowledge graphs improved its performance further.

Source: Healthcare Press (healthcarepress.net)

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