Automated patient monitoring and diagnosis assistance by integrating statistical and artificial intelligence tools

Ali Cinar; Eric Tatara; Jeffrey DeCicco; Raghu Raj; Neil Aggarwal; Michelle Chesebro; Jennifer Evans; Miraj Shah-Khan; Andrew Zloza

Automated patient monitoring and diagnosis assistance by integrating statistical and artificial intelligence tools

Ali Cinar, Eric Tatara, Jeffrey DeCicco, Raghu Raj, Neil Aggarwal, Michelle Chesebro, Jennifer Evans, Miraj Shah-Khan, Andrew Zloza

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

Abstract

Automated data collection from patients has created new challenges for health care professionals in their efforts to extract uses information from raw data. Online monitoring devices may generate large amounts of data that must be interpreted quickly and accurately. The use of statistical methods and artificial intelligence (AI) tools to summarize and interpret high frequency physiologic data such as the electrocardiogram (EKG) are investigated. The development of a methodology and associated tools for real-time patient data monitoring and diagnosis assistance was accomplished by using MATLAB and G2, a real-time knowledge-based system (KBS) development shell. A KBS was developed that incorporates various DSP and statistical methods with a rule-based decision system to detect abnormal situations, provide preliminary interpretation and diagnosis assistance, and to report these findings to medical personnel.

Original language	English (US)
Title of host publication	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Publisher	IEEE
Pages	700
Number of pages	1
ISBN (Print)	0780356756
State	Published - 1999
Externally published	Yes
Event	Proceedings of the 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Fall Meeting of the Biomedical Engineering Society (1st Joint BMES / EMBS) - Atlanta, GA, USA Duration: Oct 13 1999 → Oct 16 1999

Publication series

Name	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume	2
ISSN (Print)	0589-1019

Other

Other	Proceedings of the 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Fall Meeting of the Biomedical Engineering Society (1st Joint BMES / EMBS)
City	Atlanta, GA, USA
Period	10/13/99 → 10/16/99

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Cite this

Cinar, A., Tatara, E., DeCicco, J., Raj, R., Aggarwal, N., Chesebro, M., Evans, J., Shah-Khan, M., & Zloza, A. (1999). Automated patient monitoring and diagnosis assistance by integrating statistical and artificial intelligence tools. In Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings (pp. 700). (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings; Vol. 2). IEEE.

Automated patient monitoring and diagnosis assistance by integrating statistical and artificial intelligence tools. / Cinar, Ali; Tatara, Eric; DeCicco, Jeffrey et al.
Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. IEEE, 1999. p. 700 (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings; Vol. 2).

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

Cinar, A, Tatara, E, DeCicco, J, Raj, R, Aggarwal, N, Chesebro, M, Evans, J, Shah-Khan, M & Zloza, A 1999, Automated patient monitoring and diagnosis assistance by integrating statistical and artificial intelligence tools. in Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, vol. 2, IEEE, pp. 700, Proceedings of the 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Fall Meeting of the Biomedical Engineering Society (1st Joint BMES / EMBS), Atlanta, GA, USA, 10/13/99.

Cinar A, Tatara E, DeCicco J, Raj R, Aggarwal N, Chesebro M et al. Automated patient monitoring and diagnosis assistance by integrating statistical and artificial intelligence tools. In Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. IEEE. 1999. p. 700. (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings).

Cinar, Ali ; Tatara, Eric ; DeCicco, Jeffrey et al. / Automated patient monitoring and diagnosis assistance by integrating statistical and artificial intelligence tools. Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. IEEE, 1999. pp. 700 (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings).

@inproceedings{b0096fa212c444c9b46377c892e9f2f3,

title = "Automated patient monitoring and diagnosis assistance by integrating statistical and artificial intelligence tools",

abstract = "Automated data collection from patients has created new challenges for health care professionals in their efforts to extract uses information from raw data. Online monitoring devices may generate large amounts of data that must be interpreted quickly and accurately. The use of statistical methods and artificial intelligence (AI) tools to summarize and interpret high frequency physiologic data such as the electrocardiogram (EKG) are investigated. The development of a methodology and associated tools for real-time patient data monitoring and diagnosis assistance was accomplished by using MATLAB and G2, a real-time knowledge-based system (KBS) development shell. A KBS was developed that incorporates various DSP and statistical methods with a rule-based decision system to detect abnormal situations, provide preliminary interpretation and diagnosis assistance, and to report these findings to medical personnel.",

author = "Ali Cinar and Eric Tatara and Jeffrey DeCicco and Raghu Raj and Neil Aggarwal and Michelle Chesebro and Jennifer Evans and Miraj Shah-Khan and Andrew Zloza",

year = "1999",

language = "English (US)",

isbn = "0780356756",

series = "Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings",

publisher = "IEEE",

pages = "700",

booktitle = "Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings",

note = "Proceedings of the 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Fall Meeting of the Biomedical Engineering Society (1st Joint BMES / EMBS) ; Conference date: 13-10-1999 Through 16-10-1999",

}

TY - GEN

T1 - Automated patient monitoring and diagnosis assistance by integrating statistical and artificial intelligence tools

AU - Cinar, Ali

AU - Tatara, Eric

AU - DeCicco, Jeffrey

AU - Raj, Raghu

AU - Aggarwal, Neil

AU - Chesebro, Michelle

AU - Evans, Jennifer

AU - Shah-Khan, Miraj

AU - Zloza, Andrew

PY - 1999

Y1 - 1999

N2 - Automated data collection from patients has created new challenges for health care professionals in their efforts to extract uses information from raw data. Online monitoring devices may generate large amounts of data that must be interpreted quickly and accurately. The use of statistical methods and artificial intelligence (AI) tools to summarize and interpret high frequency physiologic data such as the electrocardiogram (EKG) are investigated. The development of a methodology and associated tools for real-time patient data monitoring and diagnosis assistance was accomplished by using MATLAB and G2, a real-time knowledge-based system (KBS) development shell. A KBS was developed that incorporates various DSP and statistical methods with a rule-based decision system to detect abnormal situations, provide preliminary interpretation and diagnosis assistance, and to report these findings to medical personnel.

AB - Automated data collection from patients has created new challenges for health care professionals in their efforts to extract uses information from raw data. Online monitoring devices may generate large amounts of data that must be interpreted quickly and accurately. The use of statistical methods and artificial intelligence (AI) tools to summarize and interpret high frequency physiologic data such as the electrocardiogram (EKG) are investigated. The development of a methodology and associated tools for real-time patient data monitoring and diagnosis assistance was accomplished by using MATLAB and G2, a real-time knowledge-based system (KBS) development shell. A KBS was developed that incorporates various DSP and statistical methods with a rule-based decision system to detect abnormal situations, provide preliminary interpretation and diagnosis assistance, and to report these findings to medical personnel.

UR - http://www.scopus.com/inward/record.url?scp=0033330722&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0033330722&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0033330722

SN - 0780356756

T3 - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

SP - 700

BT - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

PB - IEEE

T2 - Proceedings of the 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Fall Meeting of the Biomedical Engineering Society (1st Joint BMES / EMBS)

Y2 - 13 October 1999 through 16 October 1999

ER -

Automated patient monitoring and diagnosis assistance by integrating statistical and artificial intelligence tools

Abstract

Publication series

Other

ASJC Scopus subject areas

Other files and links

Fingerprint

Cite this