Classification and feature extraction for text-based drug incident report

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Medical institutions have been constructed incident report system, then accumulating incident data. Incident data compose text-based data and some structured attributes. We considered based on the analysis result with clustering for drug incident report. Firstly, we generated a network of documents and words from the text-based data. Secondly, Louvain method was applied to the network and 11 clusters were generated. We confirmed the contents of each cluster from feature words extracted by TF-IDF. Then, we compare clusters of text-based data with structured attributes and grasp the trend of the incident. This proposed method showed the possibility of clinical support toward reduction incident from text-based data.

Original languageEnglish
Title of host publicationProceedings of 2018 6th International Conference on Bioinformatics and Computational Biology, ICBCB 2018
PublisherAssociation for Computing Machinery
Pages145-149
Number of pages5
ISBN (Electronic)9781450363488
DOIs
Publication statusPublished - Mar 12 2018
Event6th International Conference on Bioinformatics and Computational Biology, ICBCB 2018 - Chengdu, China
Duration: Mar 12 2018Mar 14 2018

Publication series

NameACM International Conference Proceeding Series

Other

Other6th International Conference on Bioinformatics and Computational Biology, ICBCB 2018
CountryChina
CityChengdu
Period3/12/183/14/18

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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  • Cite this

    Yamashita, T., Nakashima, N., & Hirokawa, S. (2018). Classification and feature extraction for text-based drug incident report. In Proceedings of 2018 6th International Conference on Bioinformatics and Computational Biology, ICBCB 2018 (pp. 145-149). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3194480.3194499