Abstract
The aim of this work is to infer a model able to extract cause-effect relations between drugs and diseases. A two-level system is proposed. The first level carries out a shallow analysis of Electronic Health Records (EHRs) in order to identify medical concepts such as drug brand-names, substances, diseases, etc. Next, all the combination pairs formed by a concept from the group of drugs (drug and substances) and the group of diseases (diseases and symptoms) are characterised through a set of 57 features. A supervised classifier inferred on those features is in charge of deciding whether that pair represents a cause-effect type of event. One of the challenges of this work is the fact that the system explores the entire document. The contributions of this paper stand on the use of real EHRs to discover adverse drug reaction events even in different sentences. Besides, the work focuses on Spanish language.
Cite
CITATION STYLE
Santiso, S., Pérez, A., Gojenola, K., Casillas, A., & Oronoz, M. (2014). Adverse drug event prediction combining shallow analysis and machine learning. In Proceedings of the 5th International Workshop on Health Text Mining and Information Analysis, Louhi 2014 at the 14th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2014 (pp. 85–89). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/w14-1113
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