Sanidhya77/n2c2_2018_track2
The National NLP Clinical Challenges (n2c2), organized in 2018, continued the legacy of i2b2 (Informatics for Biology and the Bedside), adding 2 new tracks and 2 new sets of data to the shared tasks organized since 2006. Track 2 of 2018 n2c2 shared tasks focused on the extraction of medications, with their signature information, and adverse drug events (ADEs) from clinical narratives. This track built on our previous medication challenge, but added a special focus on ADEs. ADEs are injuries resulting from a medical intervention related to a drugs and can include allergic reactions, drug interactions, overdoses, and medication errors. Collectively, ADEs are estimated to account for 30% of all hospital adverse events; however, ADEs are preventable. Identifying potential drug interactions, overdoses, allergies, and errors at the point of care and alerting the caregivers of potential ADEs can improve health delivery, reduce the risk of ADEs, and improve health outcomes. A step in this direction requires processing narratives of clinical records that often elaborate on the medications given to a patient, as well as the known allergies, reactions, and adverse events of the patient. Extraction of this information from narratives complements the structured medication information that can be obtained from prescriptions, allowing a more thorough assessment of potential ADEs before they happen. The 2018 n2c2 shared task Track 2, hereon referred to as the ADE track, tackled these natural language processing tasks in 3 different steps, which we refer to as tasks: 1. Concept Extraction: identification of concepts related to medications, their signature information, and ADEs 2. Relation Classification: linking the previously mentioned concepts to their medication by identifying relations on gold standard concepts 3. End-to-End: building end-to-end systems that process raw narrative text to discover concepts and find relations of those concepts to their medications Shared tasks provide a venue for head-to-head comparison of systems developed for the same task and on the same data, allowing researchers to identify the state of the art in a particular task, learn from it, and build on it.
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