  Interventional cancer clinical trials are generally too restrictive, and some
patients are often excluded on the basis of comorbidity, past or concomitant
treatments, or the fact that they are over a certain age. The efficacy and
safety of new treatments for patients with these characteristics are,
therefore, not defined. In this work, we built a model to automatically predict
whether short clinical statements were considered inclusion or exclusion
criteria. We used protocols from cancer clinical trials that were available in
public registries from the last 18 years to train word-embeddings, and we
constructed a~dataset of 6M short free-texts labeled as eligible or not
eligible. A text classifier was trained using deep neural networks, with
pre-trained word-embeddings as inputs, to predict whether or not short
free-text statements describing clinical information were considered eligible.
We additionally analyzed the semantic reasoning of the word-embedding
representations obtained and were able to identify equivalent treatments for a
type of tumor analogous with the drugs used to treat other tumors. We show that
representation learning using {deep} neural networks can be successfully
leveraged to extract the medical knowledge from clinical trial protocols for
potentially assisting practitioners when prescribing treatments.
