upload hub_repos/n2c2_2018_track2/README.md to hub from bigbio repo

README.md

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+
+---
+language: 
+- en
+license: other
+license_bigbio_shortname: DUA
+pretty_name: n2c2 2018 ADE
+---
+
+
+# Dataset Card for n2c2 2018 ADE
+
+## Dataset Description
+
+- **Homepage:** https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/
+- **Pubmed:** False
+- **Public:** False
+- **Tasks:** Named Entity Recognition, Relation Extraction
+
+
+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.
+
+
+
+## Citation Information
+
+```
+@article{DBLP:journals/jamia/HenryBFSU20,
+  author    = {
+                Sam Henry and
+                Kevin Buchan and
+                Michele Filannino and
+                Amber Stubbs and
+                Ozlem Uzuner
+               },
+  title     = {2018 n2c2 shared task on adverse drug events and medication extraction
+               in electronic health records},
+  journal   = {J. Am. Medical Informatics Assoc.},
+  volume    = {27},
+  number    = {1},
+  pages     = {3--12},
+  year      = {2020},
+  url       = {https://doi.org/10.1093/jamia/ocz166},
+  doi       = {10.1093/jamia/ocz166},
+  timestamp = {Sat, 30 May 2020 19:53:56 +0200},
+  biburl    = {https://dblp.org/rec/journals/jamia/HenryBFSU20.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+
+```