Explainable Case-based Diagnosis and Risk Prediction in Oncology

In medical domains, precision medicine highlights the importance of diagnostic, prognostic, and therapeutic choices tailored to each patient, based on the multimodal data available. In particular, so-called confounding factors such as demographic and socio-economic factors affect the outcome, so that their inclusion in a predictive model is of paramount importance. In precision oncology, genetic data play a key role, along with confounding factors, so that large amounts of such data is available to researchers to help decipher the association between multifactorial variables and diagnosis, prognosis, and therapy. This project demonstrates that a case-based approach can be advantageous in the handling of confounding factors and in explainability, not to mention model efficiency. The effectiveness of the case-based approach is demonstrated on both diagnostic and prognostic tasks in comparison with state-of-the-art statistical and machine learning models while affording precise user explanations at the case level.