Patient disease identification in clinical notes

Jinhe Shi, Yi Chen, Guodong Gordon Gao, P. Kenyon Crowley, William C. Kinsman, Chenyu Ha, Chelsea N. King, Eric Sullivan

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

Abstract

This poster presents an innovative model for patient disease identification from clinical notes. CLSTM-Attention leverages the rich context information and learn the features automatically to extract the disease information of patients. Preliminary evaluation verified the effectiveness of the approach.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages440
Number of pages1
ISBN (Electronic)9781538653777
DOIs
StatePublished - Jul 24 2018
Event6th IEEE International Conference on Healthcare Informatics, ICHI 2018 - New York, United States
Duration: Jun 4 2018Jun 7 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018

Other

Other6th IEEE International Conference on Healthcare Informatics, ICHI 2018
CountryUnited States
CityNew York
Period6/4/186/7/18

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Health Informatics

Keywords

  • Clinical notes
  • Deep learning
  • Disease identification

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