title: A New Data Representation Based on Training Data Characteristics to Extract Drug Name Entity in Medical Text.
abstract: 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 mining poses more challenges, for example, more unstructured text, the fast growing of new terms addition, a wide range of name variation for the same drug, the lack of labeled dataset sources and external knowledge, and the multiple token representations for a single drug name. Although many approaches have been proposed to overwhelm the task, some problems remained with poorF-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, that is, MLP. The second technique involves two deep network classifiers, that is, DBN and SAE. The third technique represents the sentence as a sequence that is evaluated with a recurrent NN model, that is, LSTM. In extracting the drug name entities, the third technique gives the bestF-score performance compared to the state of the art, with its averageF-score being 0.8645.
authors.affiliation: Faculty of Computer Science, Universitas Mercu Buana, l. Meruya Selatan No. 1, Kembangan, Jakarta Barat 11650, Indonesia; Machine Learning and Computer Vision Laboratory, Faculty of Computer Science, Universitas Indonesia, Depok, West Java 16424, Indonesia.
authors.forename: Mujiono
authors.identifier: 0000-0003-0639-5245
authors.initials: M
authors.lastname: Sadikin
authors.affiliation: Machine Learning and Computer Vision Laboratory, Faculty of Computer Science, Universitas Indonesia, Depok, West Java 16424, Indonesia.
authors.forename: Mohamad Ivan
authors.identifier: 0000-0002-6050-664X
authors.initials: MI
authors.lastname: Fanany
authors.affiliation: Machine Learning and Computer Vision Laboratory, Faculty of Computer Science, Universitas Indonesia, Depok, West Java 16424, Indonesia.
authors.forename: T
authors.initials: T
authors.lastname: Basaruddin
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D057225:Data Mining / Q000706:statistics & numerical data*; D006801:Humans; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing