Pharmacovigilance And Clinical Trials Reports
Pharmacovigilance (PV) is outlined by the planet
Health Organization because the science and activities associated with the detection, assessment, understanding and hindrance of adverse effects or the other drug-related drawback. a necessary side in PV is to amass data concerning Drug–Drug Interactions (DDIs). The shared tasks on DDI-Extraction organized in 2011 and 2013 have known the importance of this issue and provided benchmarks for: Drug Name Recognition, DDI extraction and DDI classification. During this paper, we have a tendency to gift our text
mining systems for these tasks and measure their results on the DDI-Extraction benchmarks. Our systems have confidence
machine learning techniques victimisation each feature-based and kernel-based ways. The obtained results for drug name recognition are encouraging. For DDI-Extraction, our hybrid system combining a feature-based methodology and a kernel-based methodology was hierarchic second within the DDI-Extraction-2011 challenge, and our ballroom dance system for DDI detection and classification was hierarchic 1st within the DDI-Extraction-2013 task at SemEval. We have a tendency to discuss our ways and results and provides tips that could future work.
Biomedical literature and clinical reports offer a natural ground to observe and analyze DDIs at a giant scale. But creating use of such immense amount of knowledge needs the look of economical and automatic tools that may assist human consultants within the discovery and follow-up of DDIs. The DDI-Extraction-2011 and DDI-Extraction-2013 shared tasks notably underlined the importance of the extraction of DDIs from medical texts.
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