Patient disease identification in clinical notes

Authors

Jinhe Shi, New Jersey Institute of Technology
Yi Chen, New Jersey Institute of Technology
Guodong Gordon Gao, University of Maryland, College Park
P. Kenyon Crowley, University of Maryland, College Park
William C. Kinsman, Inovalon
Chenyu Ha, Inovalon
Chelsea N. King, Inovalon
Eric Sullivan, Inovalon

Document Type

Conference Proceeding

Publication Date

7-24-2018

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.

Identifier

85051104967 (Scopus)

ISBN

[9781538653777]

Publication Title

Proceedings 2018 IEEE International Conference on Healthcare Informatics Ichi 2018

External Full Text Location

https://doi.org/10.1109/ICHI.2018.00090

First Page

440

Recommended Citation

Shi, Jinhe; Chen, Yi; Gao, Guodong Gordon; Crowley, P. Kenyon; Kinsman, William C.; Ha, Chenyu; King, Chelsea N.; and Sullivan, Eric, "Patient disease identification in clinical notes" (2018). Faculty Publications. 8505.
https://digitalcommons.njit.edu/fac_pubs/8505