Artificial neural network cardiopulmonary modeling and diagnosis

Title: Artificial neural network cardiopulmonary modeling and diagnosis

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Abstract

The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.

Inventors:

Kangas, Lars J ^[1]; Keller, Paul E ^[1]

Richland, WA

Issue Date:: 1997-01-01

Research Org.:: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

OSTI Identifier:: 871203

Patent Number(s):: 5680866

Assignee:: Battelle Memorial Institute (Richland, WA)

Patent Classifications (CPCs):: G - PHYSICS G16 - INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS G16H - HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

Y - NEW / CROSS SECTIONAL TECHNOLOGIES Y10 - TECHNICAL SUBJECTS COVERED BY FORMER USPC Y10S - TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS

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G - PHYSICS G16 - INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS G16H - HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
G16H15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof

Y - NEW / CROSS SECTIONAL TECHNOLOGIES Y10 - TECHNICAL SUBJECTS COVERED BY FORMER USPC Y10S - TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
Y10S706/924 - Medical

G - PHYSICS G16 - INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS G16H - HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
G16H10/60 - for patient-specific data, e.g. for electronic patient records
G16H50/20 - for computer-aided diagnosis, e.g. based on medical expert systems

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DOE Contract Number:: AC06-76RL01830

Resource Type:: Patent

Country of Publication:: United States

Language:: English

Subject:: artificial; neural; network; cardiopulmonary; modeling; diagnosis; method; diagnosing; condition; individual; comparing; data; progressive; multi-stage; non-linear; multi-variate; model; preferably; recurrent; sensor; fusion; relies; cardiovascular; developed; physiological; measurements; differences; modeled; parameters; time; artificial neural; neural network; neural net; /600/706/

Citation Formats


                    Kangas, Lars J, and Keller, Paul E. Artificial neural network cardiopulmonary modeling and diagnosis.  United States: N. p., 1997. 
        Web.

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                    Kangas, Lars J, & Keller, Paul E. Artificial neural network cardiopulmonary modeling and diagnosis.  United States.

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                    Kangas, Lars J, and Keller, Paul E. Wed .  
        "Artificial neural network cardiopulmonary modeling and diagnosis".  United States.  https://www.osti.gov/servlets/purl/871203.

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@article{osti_871203,

  title        = {Artificial neural network cardiopulmonary modeling and diagnosis},

  author       = {Kangas, Lars J and Keller, Paul E},

  abstractNote = {The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.},

  doi          = {},

  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {1997},

  month        = {1}

}

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