# Clinical Trial Classification of SNS24 Calls with Neural Networks
## 2 Literature Review  
Applying machine learning and deep learning technologies in healthcare domain is an active research topic [2]. A wide variety of paradigms, such as linear, probabilistic, and neural networks, has been proposed for clinical classification problems. For examples, [PERSON] et al. [3] used machine learning techniques for patient stratification and phenotype investigation in rare diseases; [PERSON] et al. [4] used machine learning techniques for cancer diagnosis. While applying traditional machine learning methods on clinical text classification, feature engineering is generally carried out using different forms of knowledge sources or rules. However, these approaches usually could not automatically learn effective features. Recently, neural network methods which show capable feature learning ability have been successfully applied to clinical domain classification. Some works have also comparedthe performance between deep learning and shallow machine learning approaches and presented better performances when using the former approaches. Research work by [PERSON] et al. [5] shows that the overall performance of the systems can be improved using Deep Learning architectures. They used neural network models to classify radiology electronic health records. They compared two neural network classification models, CNN (convolution neural network) and NAM (neural attention mechanism), with the baseline SVM model. On average, the two neural models (CNN and NAM) achieved an accuracy of 87% and an improvement of 3% over the SVM baseline model. [PERSON] and [PERSON] [6] used CNN in predicting the single underlying cause of death from a list of relevant medical conditions. They worked on a dataset containing 1,499,128 records and 1180 possible classes as causes of death. They compared the CNN models to several shallow classifiers (SVM, naive bayes, random forest, and traditional BoW classification techniques). The CNN model achieved around 75% accuracy and was able to outperform all other shallow classifier models. [PERSON] et al. [7] presented a convolutional neural network for multi-label document classification, aiming to predict the most relevant segments for the possible medical codes from clinical texts. They evaluated the approach on the MIMIC-II and MIMIC-III datasets, two open-access datasets of texts and structured records from the hospital ICU. Their method obtained a precision of 0.71 and a micro-F1 of 0.54 on the MIMIC-III dataset, and a precision of 0.52 and a micro-F1 of 0.44 on the MIMIC-II dataset. [PERSON] et al. [8] used a CNN-based approach for sentence-level classification of medical documents into one of the 26 categories. Their results showed that when compared with the shallow learning methods, the CNN-based approach captured more complex features to represent the semantics of the sentence. The CNN-based model achieves an accuracy of 68%, the highest score among all the tested models. [PERSON] et al. [9] researched on using CNN models to classify biomedical texts, and the experiments were carried out on a cancer related dataset. Their evaluations showed that a basic CNN model achieved competitive performance compared with an SVM (Support Vector Machine) trained using manually optimized engineered features. The CNN-based model outperformed the SVM with modifications to the CNN hyperparameters, initialization, and training process. [PERSON] et al. [10] experimented an integrated CNN-RNN model to provide patients with pre-diagnosis suggestions or clinic guidance online. Their experiments were carried out on the available online medical data. CNN, RNN, RCNN (Region CNN), CRNN (Convolutional RNN), and CNN-RNN models were compared. The integrated CNN-RNN model improved classification precision and was efficient in training efficiency. [PERSON] et al. [11] explored using CNN, RNN, and RCNN in clinical department recommendations. The experiments were carried out on a local constructed corpus consisting with 20 thousands patient symptom descriptions.