One essential task in information extraction from the medical corpus is drug name recognition. Compared with text sources come from other domains, the medical text is special and has unique characteristics. In addition, the medical text mining poses more challenges, e.g., more unstructured text, the fast growing of new terms addition, a wide range of name variation for the same drug. The mining is even more challenging due to the lack of labeled dataset sources and external knowledge, as well as multiple token representations for a single drug name that is more common in the real application setting. Although many approaches have been proposed to overwhelm the task, some problems remained with poor F-score performance (less than 0.75). This paper presents a new treatment in data representation techniques to overcome some of those challenges. We propose three data representation techniques based on the characteristics of word distribution and word similarities as a result of word embedding training. The first technique is evaluated with the standard NN model, i.e., MLP (Multi-Layer Perceptrons). The second technique involves two deep network classifiers, i.e., DBN (Deep Belief Networks), and SAE (Stacked Denoising Encoders). The third technique represents the sentence as a sequence that is evaluated with a recurrent NN model, i.e., LSTM (Long Short Term Memory). In extracting the drug name entities, the third technique gives the best F-score performance compared to the state of the art, with its average F-score being 0.8645.
Another approach for discovering valuable information from clinical text data that adopts event-location extraction model was examined by J, @cite_10 . They use an SVM classifier to predict drug or non-drug entity which is applied to DrugBank dataset. The best performance achieved by their method is a 0.6 in F-score. The drawback of their approach is that it only deals with a single token drug name.
@cite_10
2250619635
The DDIExtraction 2013 task in the SemEval conference concerns the detection of drug names and statements of drug-drug interactions (DDI) from text. Extraction of DDIs is important for providing up-to-date knowledge on adverse interactions between coadministered drugs. We apply the machine learning based Turku Event Extraction System to both tasks. We evaluate three feature sets, syntactic features derived from deep parsing, enhanced optionally with features derived from DrugBank or from both DrugBank and MetaMap. TEES achieves F-scores of 60 for the drug name recognition task and 59 for the DDI extraction task.