TY - GEN
T1 - Using Structured event to represent complaints of patients
T2 - 5th IEEE International Conference on Computer and Communications, ICCC 2019
AU - Song, Haowei
AU - Li, Gangmin
AU - Liu, Zuopeng
AU - Bai, Xuming
N1 - Funding Information:
This work is partially supported by the AI University Research Centre (AI-URC) through XJTLU Key Programme Special Fund (KSF-P-02) and KSF-A-17. And it is also partially supported by Suzhou Science and Technology Programme Key Industrial Technology Innovation programme with project code SYG201840. We appreciate their support and guidance.
Funding Information:
This work is partially supported by the AI University Research Centre (AI-URC) through XJTLU Key Programme Special Fund (KSF-P-02) and KSF-A-17
Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Extracting relations between entities from complaints of patients is a significant but challenging problem in intelligent medical diagnose. It can help doctors to record the main information from the complaints of patients. As the development of technologies, Doctors need a more effective and convenient way to capture entire information from patients' complaints and build Electronic Health Records (EHRs). This paper proposes an event generation model which input the complaints of patients directly then output a series of events as complementary to traditional keywords based chief complaint capture. The event generation model adopts an open Chinese Information Extraction (open Chinese IE) and build a part-of-speech tagging to do dependency grammar analysis. Two kinds of evaluations are taken. One is metrics recall-oriented understanding for gusting evaluation (ROUGE). It measures the fitness of the generated events with the standard reference from doctors. The other are accuracy and Matthews Correlation Coefficient (MCC). They test the performance of grammar analysis. The results show our model have an excellent and robust performance.
AB - Extracting relations between entities from complaints of patients is a significant but challenging problem in intelligent medical diagnose. It can help doctors to record the main information from the complaints of patients. As the development of technologies, Doctors need a more effective and convenient way to capture entire information from patients' complaints and build Electronic Health Records (EHRs). This paper proposes an event generation model which input the complaints of patients directly then output a series of events as complementary to traditional keywords based chief complaint capture. The event generation model adopts an open Chinese Information Extraction (open Chinese IE) and build a part-of-speech tagging to do dependency grammar analysis. Two kinds of evaluations are taken. One is metrics recall-oriented understanding for gusting evaluation (ROUGE). It measures the fitness of the generated events with the standard reference from doctors. The other are accuracy and Matthews Correlation Coefficient (MCC). They test the performance of grammar analysis. The results show our model have an excellent and robust performance.
KW - complaints of patients
KW - component
KW - event generation model
KW - open Chinese IE
UR - http://www.scopus.com/inward/record.url?scp=85084050359&partnerID=8YFLogxK
U2 - 10.1109/ICCC47050.2019.9064234
DO - 10.1109/ICCC47050.2019.9064234
M3 - Conference Proceeding
AN - SCOPUS:85084050359
T3 - 2019 IEEE 5th International Conference on Computer and Communications, ICCC 2019
SP - 2193
EP - 2197
BT - 2019 IEEE 5th International Conference on Computer and Communications, ICCC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 6 December 2019 through 9 December 2019
ER -