## 2 Related Work  
Clinical concept extraction refers to automatically extracting concepts of interest from unstructured clinical texts [15]. Concept extraction can also be referred to as Named Entity Recognition (NER) [16]. In the cancer domain, research to extract cancer concepts from clinical narratives has increased considerably in recent years due to its benefits associated to evidence-based research and quality improvement [17]. Clinical concept extraction is commonly addressed by using rules, machine learning, or deep learning approaches. One of the first interests in the field of oncology was identifying a patient's cancer stage, which is an important prognostic factor in order to understand cancer-survival. In [18; 19], a rule-based method to extract data associated to a tumor's stage from clinical documents of lung cancer patients is presented. Meanwhile, in [20], a different rule-based tool for extracting cancer staging from pathological reports of breast cancer using the TNM ([[https://www.cancer.gov/about-cancer/diagnosis-staging/staging](https://www.cancer.gov/about-cancer/diagnosis-staging/staging)]([https://www.cancer.gov/about-cancer/diagnosis-staging/staging](https://www.cancer.gov/about-cancer/diagnosis-staging/staging))) notation is described. Other studies related to the extraction of a cancer stage are described in [21; 22; 23]. Although the stage is an important cancer factor, extracting only this concept is not enough to fully understand cancer behavior. Other proposals have attempted to extract more concepts associated with cancer. These approaches focus on the extraction of cancer diagnosis [9; 24], treatments [25; 26], or both [4; 5]. In [27], the authors proposed a strategy to extract breast cancer diagnosis, the histology of malignant neoplasm, temporal expressions, and recurrent cancer-related events. In [25], a machine learning approach to extract cancer treatments, doses, toxicities, and dates from lung cancer patients is proposed. Other machine learning-based approaches to extract cancer information are proposed in [28; 29]. Recently, Deep neural networks have been shown to improve performance in the processing of natural language texts. More specifically, Recurrent neural networks (RNN) [30; 31; 32] and Convolution Neural Networks (CNN) [33] have been used to improve text processing. Deep learning approaches have also shown their applicability in different domains such as finance [34], smart cities [35], and security [36]. Besides, word embedding is a useful tool to create deep learning applications [37; 38; 39], and it has gained great popularity in the biomedical field [40; 41]. In [4] a deep learning-based approach to extract lung cancer stages, histology, tumor grades and therapies (chemotherapy, radiotherapy, surgery) is proposed. Clinical notes, pathology reports, and surgery reports are used to test the system. The authors highlight the feasibility of extracting cancer-related information from clinical narratives and the feasibility of improving the efficiency of humans through NLP techniques. A Bidirectional Long Short-Term Memory (BiLSTM) network is used in [5] to extract a comprehensive set of breast cancer concepts. This proposal extracts more than forty concepts which help to understand the behavior of this disease. Although deep learning-based approaches have improved the ability to extract medical concepts, there is still a gap between concept extraction and concept understanding [6]. Extracting cancer-related concepts alone is not effective in understanding clinical events related to patients. New steps are required to go beyond clinical concept extraction in order to understand relationships between events and concepts in the natural history of a patient. In addition, most of the proposals mentioned above have focused on the English language. According to [42], information extraction in the medical domain also represents its own challenges in languages other than English.