## 8 Discussion  
Automatically extracting lung cancer diagnosis from clinical notes can be useful to support clinical research as establishing the diagnosis is crucial to understand factors occurring prior and after the disease. The approach proposed in this paper contributes to automatically extract diagnosis and dates from clinical narratives written in Spanish. The use of deep learning methods was effective for named entity recognition in the cancer domain. The performance of the BiLSTM-CRF model has shown promising results as it can see in Table 3. Specifically, including the Event entity was useful to disambiguate sentences containing several date mentions. Thus our approach outperforms the proposal presented in [10] as the latter does not take into account event extraction. Besides, it is important to note that once a neural network has been trained with lung cancer-related data it can be used to learn not only on lung cancer notes but also on other kind of tumors. On the other hand, the rule-based approach presented has shown to return competitive results for speculation and negation detection tasks. In particular, the following facts should be stressed:  
* The proposed approach uses a lexicon that has been adapted to Spanish from two different resources specialized in negation and speculation in the biomedical domain. Additionally, the lexicon was semantically extended with a word embeddings technique that helped obtain new cues with similar meanings. Moreover, this lexicon was manually reviewed and evaluated. The resulting lexicon offers high precision in the cue detection task. * Some weaknesses reported in [12], such as _post-scope recognition_ are addressed in this proposal by using Regex 2 (see Section 5.2). When a cue is detected using this Regex, the scope is searched to the left of the cue. * The rules proposed for scope recognition are adapted according to the analysis that has been performed on how speculation and negation are expressed in clinical notes written in Spanish (see Section 5.3). Despite the promising results, still some limitations have to be addressed. In particular, one of the main challenges for using deep learning approaches is that one must have an annotated corpus. In this paper, a manually annotated corpus to support lung cancer diagnosis extraction was proposed. However, a larger corpus containing more annotations should be addressed in future experiments. Moreover, to extract more information regarding lung cancer disease, a more comprehensive set of entities would be needed. In the speculation cue detection, one of the challenges to be solved relates to the detecting of syntactic speculation cues, specifically the token _\"o\"_ (or). This token does \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment** & **P** & **R** & **F1** \\ \hline Using all Named Entities & 0.67 & 0.62 & 0.64 \\ Filtering negated entities & 0.72 & 0.71 & 0.71 \\ Filtering negated and speculated entities & **0.92** & **0.87** & **0.89** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results for extracting the diagnosis date. not behave as a speculation cue in more than 99% of cases. In order to illustrate this, consider the following sentences:  
1. _\"Causas de sangrado: una complicacion de la cirugita o una patologia asociada al cancer de pulmon.\"_ (Causes of bleeding: a surgery-related complication **or** a lung cancer-related pathology)
2. _\"Si acute a alguna consulta o servicio sanitario debe ser acompanado. \"_ (Patients must be accompanied when going to general appointments **or** when using the health service.)) In the first sentence, the token _\"o\"_ acts as a speculation cue, while in the second one, it acts as a disjunction element. As the second case is the most frequent, in our approach we decided not to include the token _\"o\"_ in the cue lexicon (Figure 4) in order to reduce false positives detection. However this should be improved in a future research.