May 2 2024Ohio State University
The team from The Ohio State University reported today in the journal Patterns that their model outperformed seven existing models and came up with the same treatment recommendations as four randomized clinical trials. 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. To come up with treatment effect estimates, the model considers pre-trained data overlapped with more specific information on medical conditions and therapies, and after further fine-tuning, predicts which patient outcomes would correspond to different treatments.
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