An Investigation of Interpretability Techniques for Deep Learning in Predictive Process Analytics

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This paper explores interpretability techniques for two of the most successful learning algorithms in medical decision-making literature: deep neural networks and random forests.

• We applied these algorithms in a real-world medical dataset containing information about patients with cancer, where we learn models that try to predict the type of cancer of the patient, given their set of medical activity records.
• We explored different algorithms based on neural network architectures using long short term deep neural networks, and random forests.
• Since there is a growing need to provide decision-makers understandings about the logic of predictions of black boxes, we also explored different techniques that provide interpretations for these classifiers.