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Model extraction for clinical decision support systems

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Integrating genomic, imaging and clinical data is crucial in providing more personalised diagnostic and treatment plans for cancer patients. We use a variety of machine learning methods for integrative and predictive purposes. While doing this, we want to ensure employability in clinical decision support systems by providing explanation for the recommendation we make.

Model extraction deals with extracting interpretable models from uninterpretable ones, where the former serves as the basis for providing explanation in the latter. I’m going to talk about rule extraction from neural networks in a cancer data integration scenario mentioned.

This talk is part of the Artificial Intelligence Research Group Talks (Computer Laboratory) series.

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