@inproceedings{ec4385620baa47ebb4deecc657984c7f,
title = "Patient disease identification in clinical notes",
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.",
keywords = "Clinical notes, Deep learning, Disease identification",
author = "Jinhe Shi and Yi Chen and Gao, {Guodong Gordon} and Crowley, {P. Kenyon} and Kinsman, {William C.} and Chenyu Ha and King, {Chelsea N.} and Eric Sullivan",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 6th IEEE International Conference on Healthcare Informatics, ICHI 2018 ; Conference date: 04-06-2018 Through 07-06-2018",
year = "2018",
month = jul,
day = "24",
doi = "10.1109/ICHI.2018.00090",
language = "English (US)",
series = "Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "440",
booktitle = "Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018",
address = "United States",
}