Electronic health record error prevention approach using ontology in big data

Keke Gai; Meikang Qiu; Li Chiou Chen; Meiqin Liu

doi:10.1109/HPCC-CSS-ICESS.2015.168

Electronic health record error prevention approach using ontology in big data

Keke Gai
, Meikang Qiu^*
, Li Chiou Chen
, Meiqin Liu

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

92 Citations (Scopus)

Abstract

Electronic Health Record (EHR) systems have been playing a dramatically important role in tele-health domains. One of the major benefits of using EHR systems is assisting physicians to gain patients' healthcare information and shorten the process of the medical decision making. However, physicians' inputs still have a great impact on making decisions that cannot be checked by EHR systems. This consequence can be influenced by human behaviors or physicians' knowledge structures. An efficient approach of alerting to the unusual decisions is an urgent requirement for current EHR systems. This paper proposes a schema using ontology in big data to generate an alerting mechanism to assist physicians to make a proper medical diagnosis. The proposed model is Ontology-based EHR Error Prevention Model (OEHR-EPM), which is implemented by a proposed algorithm, Error Prevention Adjustment Algorithm (EPAA). The ontological approach uses Protege to represent the knowledge-based ontology. The proposed schema has been examined by our experiments and the experimental results show that our schema has a higher-level accuracy rate and acceptable operating time performance.

Original language	English
Title of host publication	Proceedings - 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security and 2015 IEEE 12th International Conference on Embedded Software and Systems, HPCC-CSS-ICESS 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	752-757
Number of pages	6
ISBN (Electronic)	9781479989362
DOIs	https://doi.org/10.1109/HPCC-CSS-ICESS.2015.168
Publication status	Published - 23 Nov 2015
Externally published	Yes
Event	17th IEEE International Conference on High Performance Computing and Communications, IEEE 7th International Symposium on Cyberspace Safety and Security and IEEE 12th International Conference on Embedded Software and Systems, HPCC-ICESS-CSS 2015 - New York, United States Duration: 24 Aug 2015 → 26 Aug 2015

Publication series

Name	Proceedings - 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security and 2015 IEEE 12th International Conference on Embedded Software and Systems, HPCC-CSS-ICESS 2015

Conference

Conference	17th IEEE International Conference on High Performance Computing and Communications, IEEE 7th International Symposium on Cyberspace Safety and Security and IEEE 12th International Conference on Embedded Software and Systems, HPCC-ICESS-CSS 2015
Country/Territory	United States
City	New York
Period	24/08/15 → 26/08/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Big data
Cloud computing
Electronic health records
Error prevention
Ontology

Access to Document

10.1109/HPCC-CSS-ICESS.2015.168

Cite this

Gai, K., Qiu, M., Chen, L. C., & Liu, M. (2015). Electronic health record error prevention approach using ontology in big data. In Proceedings - 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security and 2015 IEEE 12th International Conference on Embedded Software and Systems, HPCC-CSS-ICESS 2015 (pp. 752-757). Article 7336248 (Proceedings - 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security and 2015 IEEE 12th International Conference on Embedded Software and Systems, HPCC-CSS-ICESS 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/HPCC-CSS-ICESS.2015.168

Gai, Keke ; Qiu, Meikang ; Chen, Li Chiou et al. / Electronic health record error prevention approach using ontology in big data. Proceedings - 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security and 2015 IEEE 12th International Conference on Embedded Software and Systems, HPCC-CSS-ICESS 2015. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 752-757 (Proceedings - 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security and 2015 IEEE 12th International Conference on Embedded Software and Systems, HPCC-CSS-ICESS 2015).

@inproceedings{74c1c2b543d245989c287c34722d87f9,

title = "Electronic health record error prevention approach using ontology in big data",

abstract = "Electronic Health Record (EHR) systems have been playing a dramatically important role in tele-health domains. One of the major benefits of using EHR systems is assisting physicians to gain patients' healthcare information and shorten the process of the medical decision making. However, physicians' inputs still have a great impact on making decisions that cannot be checked by EHR systems. This consequence can be influenced by human behaviors or physicians' knowledge structures. An efficient approach of alerting to the unusual decisions is an urgent requirement for current EHR systems. This paper proposes a schema using ontology in big data to generate an alerting mechanism to assist physicians to make a proper medical diagnosis. The proposed model is Ontology-based EHR Error Prevention Model (OEHR-EPM), which is implemented by a proposed algorithm, Error Prevention Adjustment Algorithm (EPAA). The ontological approach uses Protege to represent the knowledge-based ontology. The proposed schema has been examined by our experiments and the experimental results show that our schema has a higher-level accuracy rate and acceptable operating time performance.",

keywords = "Big data, Cloud computing, Electronic health records, Error prevention, Ontology",

author = "Keke Gai and Meikang Qiu and Chen, \{Li Chiou\} and Meiqin Liu",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 17th IEEE International Conference on High Performance Computing and Communications, IEEE 7th International Symposium on Cyberspace Safety and Security and IEEE 12th International Conference on Embedded Software and Systems, HPCC-ICESS-CSS 2015 ; Conference date: 24-08-2015 Through 26-08-2015",

year = "2015",

month = nov,

day = "23",

doi = "10.1109/HPCC-CSS-ICESS.2015.168",

language = "English",

series = "Proceedings - 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security and 2015 IEEE 12th International Conference on Embedded Software and Systems, HPCC-CSS-ICESS 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "752--757",

booktitle = "Proceedings - 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security and 2015 IEEE 12th International Conference on Embedded Software and Systems, HPCC-CSS-ICESS 2015",

address = "United States",

}

Gai, K, Qiu, M, Chen, LC & Liu, M 2015, Electronic health record error prevention approach using ontology in big data. in Proceedings - 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security and 2015 IEEE 12th International Conference on Embedded Software and Systems, HPCC-CSS-ICESS 2015., 7336248, Proceedings - 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security and 2015 IEEE 12th International Conference on Embedded Software and Systems, HPCC-CSS-ICESS 2015, Institute of Electrical and Electronics Engineers Inc., pp. 752-757, 17th IEEE International Conference on High Performance Computing and Communications, IEEE 7th International Symposium on Cyberspace Safety and Security and IEEE 12th International Conference on Embedded Software and Systems, HPCC-ICESS-CSS 2015, New York, United States, 24/08/15. https://doi.org/10.1109/HPCC-CSS-ICESS.2015.168

Electronic health record error prevention approach using ontology in big data. / Gai, Keke; Qiu, Meikang; Chen, Li Chiou et al.
Proceedings - 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security and 2015 IEEE 12th International Conference on Embedded Software and Systems, HPCC-CSS-ICESS 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 752-757 7336248 (Proceedings - 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security and 2015 IEEE 12th International Conference on Embedded Software and Systems, HPCC-CSS-ICESS 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Electronic health record error prevention approach using ontology in big data

AU - Gai, Keke

AU - Qiu, Meikang

AU - Chen, Li Chiou

AU - Liu, Meiqin

PY - 2015/11/23

Y1 - 2015/11/23

N2 - Electronic Health Record (EHR) systems have been playing a dramatically important role in tele-health domains. One of the major benefits of using EHR systems is assisting physicians to gain patients' healthcare information and shorten the process of the medical decision making. However, physicians' inputs still have a great impact on making decisions that cannot be checked by EHR systems. This consequence can be influenced by human behaviors or physicians' knowledge structures. An efficient approach of alerting to the unusual decisions is an urgent requirement for current EHR systems. This paper proposes a schema using ontology in big data to generate an alerting mechanism to assist physicians to make a proper medical diagnosis. The proposed model is Ontology-based EHR Error Prevention Model (OEHR-EPM), which is implemented by a proposed algorithm, Error Prevention Adjustment Algorithm (EPAA). The ontological approach uses Protege to represent the knowledge-based ontology. The proposed schema has been examined by our experiments and the experimental results show that our schema has a higher-level accuracy rate and acceptable operating time performance.

AB - Electronic Health Record (EHR) systems have been playing a dramatically important role in tele-health domains. One of the major benefits of using EHR systems is assisting physicians to gain patients' healthcare information and shorten the process of the medical decision making. However, physicians' inputs still have a great impact on making decisions that cannot be checked by EHR systems. This consequence can be influenced by human behaviors or physicians' knowledge structures. An efficient approach of alerting to the unusual decisions is an urgent requirement for current EHR systems. This paper proposes a schema using ontology in big data to generate an alerting mechanism to assist physicians to make a proper medical diagnosis. The proposed model is Ontology-based EHR Error Prevention Model (OEHR-EPM), which is implemented by a proposed algorithm, Error Prevention Adjustment Algorithm (EPAA). The ontological approach uses Protege to represent the knowledge-based ontology. The proposed schema has been examined by our experiments and the experimental results show that our schema has a higher-level accuracy rate and acceptable operating time performance.

KW - Big data

KW - Cloud computing

KW - Electronic health records

KW - Error prevention

KW - Ontology

UR - https://www.scopus.com/pages/publications/84961763233

U2 - 10.1109/HPCC-CSS-ICESS.2015.168

DO - 10.1109/HPCC-CSS-ICESS.2015.168

M3 - Conference contribution

AN - SCOPUS:84961763233

T3 - Proceedings - 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security and 2015 IEEE 12th International Conference on Embedded Software and Systems, HPCC-CSS-ICESS 2015

SP - 752

EP - 757

BT - Proceedings - 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security and 2015 IEEE 12th International Conference on Embedded Software and Systems, HPCC-CSS-ICESS 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 17th IEEE International Conference on High Performance Computing and Communications, IEEE 7th International Symposium on Cyberspace Safety and Security and IEEE 12th International Conference on Embedded Software and Systems, HPCC-ICESS-CSS 2015

Y2 - 24 August 2015 through 26 August 2015

ER -

Gai K, Qiu M, Chen LC, Liu M. Electronic health record error prevention approach using ontology in big data. In Proceedings - 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security and 2015 IEEE 12th International Conference on Embedded Software and Systems, HPCC-CSS-ICESS 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 752-757. 7336248. (Proceedings - 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security and 2015 IEEE 12th International Conference on Embedded Software and Systems, HPCC-CSS-ICESS 2015). doi: 10.1109/HPCC-CSS-ICESS.2015.168

Electronic health record error prevention approach using ontology in big data

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this