Advancing the State of the Art in Clinical Natural Language Processing through Shared Tasks.

@article{Filannino2018AdvancingTS,
  title={Advancing the State of the Art in Clinical Natural Language Processing through Shared Tasks.},
  author={Michele Filannino and {\"O}zlem Uzuner},
  journal={Yearbook of medical informatics},
  year={2018},
  volume={27 1},
  pages={
          184-192
        }
}
OBJECTIVES  To review the latest scientific challenges organized in clinical Natural Language Processing (NLP) by highlighting the tasks, the most effective methodologies used, the data, and the sharing strategies. METHODS  We harvested the literature by using Google Scholar and PubMed Central to retrieve all shared tasks organized since 2015 on clinical NLP problems on English data. RESULTS  We surveyed 17 shared tasks. We grouped the data into four types (synthetic, drug labels, social… CONTINUE READING

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References

Publications referenced by this paper.
Showing 1-10 of 61 references

NTTMUNSW system for adverse drug reactions extraction in Twitter data

CK Wang, ON Singh, +3 authors US Iqbal
In Proceedings of the Social Media Mining Shared Task Workshop at the Pacific Symposium on Biocomputing, • 2016
View 11 Excerpts
Highly Influenced

Health Insurance Portability and Accountability Act (HIPAA)

Encyclopedia of Information Assurance • 2011
View 4 Excerpts
Highly Influenced

NTCIR-13 MedWeb Task: Multi-label Classification of Tweets using an Ensemble of Neural Networks

H Iso, C Ruiz, +3 authors H Yamamoto
In Proceedings of the NTCIR-13 • 2017
View 1 Excerpt

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