  Open-set recognition generalizes a classification task by classifying test
samples as one of the known classes from training or "unknown." As novel cancer
drug cocktails with improved treatment are continually discovered, predicting
cancer treatments can naturally be formulated in terms of an open-set
recognition problem. Drawbacks, due to modeling unknown samples during
training, arise from straightforward implementations of prior work in
healthcare open-set learning. Accordingly, we reframe the problem methodology
and apply a recent existing Gaussian mixture variational autoencoder model,
which achieves state-of-the-art results for image datasets, to breast cancer
patient data. Not only do we obtain more accurate and robust classification
results, with a 24.5% average F1 increase compared to a recent method, but we
also reexamine open-set recognition in terms of deployability to a clinical
setting.
