## 5 Conclusions  
In this work, we have trained, validated, and compared various classifiers (FastText and a CNN with pre-trained word-embeddings, kNN, and SVM) on a corpus of cancer clinical trial protocols (www.clinicaltrials.gov). The models classify short free-text sentences describing clinical information (medical history, concomitant medication, type and features of tumor, such as molecular profile, cancer therapy, etc.) as eligible or not eligible criteria for volunteering in these trials. SVM yielded the lowest accuracy results, and kNN obtained top accuracy performance similar to the CNN model, but it had the lowest computational performance. Particularly, the high accuracy achieved with kNN is the immediate consequence of using as input a highly efficient clinical statement representation which is based on averaged pre-trained word-embeddings. A possible reason for this is that the kNN accuracy relies almost exclusively on using a highly efficient vector representation as the input data and on the dataset size. Being a non-parametric method, it is often successful--as in this case--in classification situations where the decision boundary is very irregular. Nonetheless, in spite of its high accuracy and the minimal training phase, we favor the use of deep learning architectures for classification (such as CNN) over a kNN model because of its lower computational cost during prediction time. In fact, classifying a given observation requires a rundown of the whole dataset being too computationally expensive for large dataset as in this work. All models were evaluated using a 5-fold cross-validation on incremental sample sizes (1K, 10K, 100K, 1M, samples) and on the largest available balanced set (using undersampling) with 4.01 million labeled samples from a total of 6 million. Overall, the models proved robust and had the ability to generalize. The best performance was achieved with kNN using a balanced sampling of the whole dataset. The results fit expectations, with an \(F_{1}=0.93\) and an agreement of \(\kappa=0.84\). The fact that the CNN model outperformed FastText may be explained by its greater depth, but more efforts should be made to experiment with alternative CNN topologies. This CNN model was also evaluated on an independent clinical data source, thus paving the way toward its potential use--taking into account pending improvements--in a clinical support system for oncologists when employing their clinical notes. During the experiments, the word-embedding models achieved high-quality clusters, in addition to demonstrating their capacity for semantic reasoning, since they were able to identify the equivalent treatments for a type of tumor by means of an analogy with the drugs used to treat other tumors. These interesting reasoning qualities merit study in a future work using this dataset. The evaluation results show that clinical trial protocols related to cancer, which are freely available, can be meaningfully exploited by applying representation learning, including deep learning techniques, thus opening up the potential to explore more ambitious goals by making the additional efforts required to build the appropriate dataset. Our most immediate future work is to use a larger sample test of short clinical text from medical records for real simulation and include the effectiveness of CT interventions in the model, thus enabling us to not only predict whether or not a patient's case has been studied, but also whether the proposed treatment is expected to be effective based on the results of completed clinical trials for each indication. The problem would be a multilabel classification task, where the classes would be \"effective\" vs. \"non-effective\" and \"studied\" vs. \"non-studied\", and both could be either true or false. This would allow us to classify from four types of cases: effective and studied, potentially effective but not studied, not effective and studied, and potentially not effective and not studied.