Machine learning for prospective identification of immunotherapy related adverse events (irAEs)

The ubiquitous implementation of immunotherapy has significantly improved outcomes in the treatment of cancer patients; however, once rare adverse events from these therapies have increased in lock step. We now face an increased burden of identification on providers with limited experience in the diagnosis of irAEs. We use machine learning to develop prediction models that will aid providers in identifying patients at high risk for developing irAEs as well as for multiple downstream applications.

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