@InProceedings{sakishita-kano:2016:ClinicalNLP,
  author    = {Sakishita, Masahito  and  Kano, Yoshinobu},
  title     = {Inference of ICD Codes from Japanese Medical Records by Searching Disease Names},
  booktitle = {Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {64--68},
  abstract  = {Importance of utilizing medical information is getting increased as electronic
	health records (EHRs) are widely used nowadays. We aim to assign international
	standardized disease codes, ICD-10, to Japanese textual information in EHRs for
	users to reuse the information accurately. In this paper, we propose methods to
	automatically extract diagnosis and to assign ICD codes to Japanese medical
	records. Due to the lack of available training data, we dare employed
	rule-based methods rather than machine learning. We observed characteristics of
	medical records carefully, writing rules to make effective methods by hand. We
	applied our system to the NTCIR-12 MedNLPDoc shared task data where
	participants are required to assign ICD-10 codes of possible diagnosis in given
	EHRs. In this shared task, our system achieved the highest F-measure score
	among all participants in the most severe evaluation criteria. Through
	comparison with other approaches, we show that our approach could be a useful
	milestone for the future development of Japanese medical record processing.},
  url       = {http://aclweb.org/anthology/W16-4209}
}