## 2 Related Work  
Machine Learning (ML) and Natural Language Processing (NLP) have recently shown important progress in applications such as clinical decision support systems by improving the quality of information processing from clinical narratives [4; 5; 6; 7; 8; 9]. In the literature we find approaches to health care data that are based on unsupervised ML models. [PERSON] et al. [10], propose a data-driven prediction model of clinical pathways for patient staying at hospitals: patients are clustered according to their movements (clinical paths) in a hospital; then, a decision tree is used to explore the existing diversity of patients' clinical pathways. In [11], Simulation Modeling and ML are used for the purpose of designing pathways and evaluating the return on investment, having a elderly hip-fracture care scheme as use case. [PERSON] et al. [12] exploit the historical component of the patient trajectory to improve the performance of clinical decision support systems; by automatically extracting information from patient medical notes they aim to assimilate detailed relevant patient information and provide recommendations during clinical treatments. Our approach, on the other hand, considers supervised models for the task of text classification. In this type of task, different paradigms and algorithms are often considered [13], and there is an objective way of measuring systems accuracy. Traditional approaches usually represent text as a bag-of-words, using a specific weighting scheme, with the most used one being TF-IDF (Term Frequency--Inverse Document Frequency); a possible generalization is using bags of n-grams. More recently, word embeddings [8] are being used to represent text, where each word is represented by a vector (of a specific size) instead of a number. As commonly known, each particular problem and dataset may have a preferred model, with no optimal general algorithm choice [14]. In this way, there are many works comparing approaches and experiment designs. One example is [PERSON] et al. 's work [15], where alternative classification algorithms and various word representations are compared regarding applications involving clinical texts. Support Vector Machines (SVM) and Deep Learning (DL) architectures usually present the best evaluation results [6; 7; 16] on evaluations performed on clinical decision support problems [17]. One known difference between these two approaches is that using ML methods (such as SVM) often requires feature engineering efforts, as these features are not learned automatically in the process. On the other hand, DL based methods have shown powerful feature learning capabilities. The literature presents many comparisons of these two types of approaches. [PERSON] et al. [18] deal with text classification on a cancer dataset. They showed that a Convolution Neural Network (CNN) model, with fine-tuned hyper-parameters, initialisation and training process, achieved better performance than an SVM model based on engineered features. [PERSON] et al. [19] present extensive comparisons of alternative models, such as active learning, SVM, Naive Bayes, and a BERT classifier applied to biomedical datasets. They found that the active learning approach reduced the number of training examples necessary for achieving the same performance of the other classifiers. Other experimental results [20] show that Bidirectional Encoder Representations from Transformers (BERT) models did not achieve better performance when compared to CNN and Hierarchical Self Attention Networks (HiSAN) models. Besides making efficient predictions, it is very important to analyse the data and explain the predictions made by the algorithm, particularly in this area of application [21; 22]. In a different way from what was found in the literature, this work presents a detailed analysis of the data, since it is useful to better understand the characteristics and the issues regarding the learned models.