|
|
|
--- |
|
language: |
|
- en |
|
bigbio_language: |
|
- English |
|
license: other |
|
multilinguality: monolingual |
|
bigbio_license_shortname: DUA |
|
pretty_name: n2c2 2018 ADE |
|
homepage: https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/ |
|
bigbio_pubmed: False |
|
bigbio_public: False |
|
bigbio_tasks: |
|
- NAMED_ENTITY_RECOGNITION |
|
- RELATION_EXTRACTION |
|
--- |
|
|
|
|
|
# Dataset Card for n2c2 2018 ADE |
|
|
|
## Dataset Description |
|
|
|
- **Homepage:** https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/ |
|
- **Pubmed:** False |
|
- **Public:** False |
|
- **Tasks:** NER,RE |
|
|
|
|
|
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} |
|
} |
|
|
|
``` |
|
|
