Extraction of potential adverse drug events from medical case reports

By Harsha Gurulingappa, Abdul Mateen‐Rajput, Luca I. G. Toldo. 2012

203 citations recorded by the source corpus.

The sheer amount of information about potential adverse drug events publishedin medical case reports pose major challenges for drug safety experts toperform timely monitoring. Efficient strategies for identification andextraction of information about potential adverse drug events fromfree‐text resources are needed to support pharmacovigilance researchand pharmaceutical decision making. Therefore, this work focusses on theadaptation of a machine learning‐based system for the identificationand extraction of potential adverse drug event relations from MEDLINE casereports. It relies on a high quality corpus that was manually annotatedusing an ontology‐driven methodology. Qualitative evaluation of thesystem showed robust results. An experiment with large scale relationextraction from MEDLINE delivered under‐identified potential adversedrug events not reported in drug monographs. Overall, this approach providesa scalable auto‐assistance platform for drug safety professionals toautomatically collect potential adverse drug events communicated asfree‐text data.

Subfields: Computational Theory and Mathematics, Molecular Biology, Toxicology

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Citations in the corpus

The SIDER database of drugs and side effects (2015, methodology): “As a further use case, SIDER has been used as the benchmarking set for text-mining methods that extract ADR data from the literature (4,11), and other databases have incorporated data from SIDER.”
Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task (2016, background): “The 25 teams represented 12 different countries in four continents: Australia (1), Asia (12), Europe (9) and North America (3).”
Automatic construction of a large-scale and accurate drug-side-effect association knowledge base from biomedical literature (2014, background): “Support vector machines are supervised machine learning models [6] and are commonly used for biomedical relationship extraction tasks, including drug-SE relationship extraction [11].”
Joint Models for Extracting Adverse Drug Events from Biomedical Text (2016, methodology): “Experimental results on a standard ADE corpus [Gurulingappa et al., 2012] show that the discrete joint model significantly outperforms a strong pipelined baseline.”
Utilizing Social Media Data for Pharmacovigilance: A Review (2015, background): “The number of unique drugs is not mentioned. i Study also includes a corpus from outside social media. assertive text—a task that has already received attention within and outside of social media [66], and will be crucial to explore within this domain as well.”