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novel NN paradigm for the prediction of hematocrit value during blood transfusion

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Date Issued:
2011
Summary:
During the Leukocytapheresis (LCAP) process used to treat patients suffering from acute Ulcerative Colitis, medical practitioners have to continuously monitor the Hematocrit (Ht) level in the blood to ensure it is within the acceptable range. The work done, as a part of this thesis, attempts to create an early warning system that can be used to predict if and when the Ht values will deviate from the acceptable range. To do this we have developed an algorithm based on the Group Method of Data Handling (GMDH) and compared it to other Neural Network algorithms, in particular the Multi Layer Perceptron (MLP). The standard GMDH algorithm captures the fluctuation very well but there is a time lag that produces larger errors when compared to MLP. To address this drawback we modified the GMDH algorithm to reduce the prediction error and produce more accurate results.
Title: A novel NN paradigm for the prediction of hematocrit value during blood transfusion.
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Name(s): Thakkar, Jay.
College of Engineering and Computer Science
Department of Computer and Electrical Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Issued: 2011
Publisher: Florida Atlantic University
Physical Form: electronic
Extent: ix, 67 p. : ill. (some col.)
Language(s): English
Summary: During the Leukocytapheresis (LCAP) process used to treat patients suffering from acute Ulcerative Colitis, medical practitioners have to continuously monitor the Hematocrit (Ht) level in the blood to ensure it is within the acceptable range. The work done, as a part of this thesis, attempts to create an early warning system that can be used to predict if and when the Ht values will deviate from the acceptable range. To do this we have developed an algorithm based on the Group Method of Data Handling (GMDH) and compared it to other Neural Network algorithms, in particular the Multi Layer Perceptron (MLP). The standard GMDH algorithm captures the fluctuation very well but there is a time lag that produces larger errors when compared to MLP. To address this drawback we modified the GMDH algorithm to reduce the prediction error and produce more accurate results.
Identifier: 746324512 (oclc), 3174078 (digitool), FADT3174078 (IID), fau:3675 (fedora)
Note(s): by Jay Thakkar.
Pagination error. "References" should be leaves 63-67, and pagination end with leaf 67.
Thesis (M.S.C.S.)--Florida Atlantic University, 2011.
Includes bibliography.
Electronic reproduction. Boca Raton, Fla., 2011. Mode of access: World Wide Web.
Subject(s): Neural networks (Computer science) -- Scientific applications
GMDH algorithms
Pattern recognition systems
Genetic algorithms
Fuzzy logic
Persistent Link to This Record: http://purl.flvc.org/FAU/3174078
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU