@InProceedings{krishna-hans:2016:Computerm2016,
  author    = {Krishna, Santosh Sai  and  Hans, Manoj},
  title     = {Understanding Medical free text: A Terminology driven approach},
  booktitle = {Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {121--125},
  abstract  = {With many hospitals digitalizing clinical records it has opened opportunities
	for researchers in NLP, Machine Learning to apply techniques for extracting
	meaning and make actionable insights. There has been previous attempts in
	mapping free text to medical nomenclature like UMLS, SNOMED. However, in this
	paper, we had analyzed diagnosis in clinical reports using ICD10 to achieve a
	lightweight, real-time predictions by introducing concepts like WordInfo, root
	word identification. We were able to achieve 68.3% accuracy over clinical
	records collected from qualified clinicians. Our study would further help the
	healthcare institutes in organizing their clinical reports based on ICD10
	mappings and derive numerous insights to achieve operational efficiency and
	better medical care.},
  url       = {http://aclweb.org/anthology/W16-4714}
}