Modelling intracranial pressure with noninvasive physiological measures
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Hughes:2017:ieeeCIBCB,
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author = "James Alexander Hughes and Ethan C. Jackson and
Mark Daley",
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booktitle = "2017 IEEE Conference on Computational Intelligence in
Bioinformatics and Computational Biology (CIBCB)",
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title = "Modelling intracranial pressure with noninvasive
physiological measures",
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year = "2017",
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abstract = "Patients who suffered a traumatic brain injury (TBI)
require special care, and physicians often monitor
intercranial pressure (ICP) as it can greatly aid in
management. Although monitoring ICP can be critical, it
requires neurosurgery, which presents additional
significant risk. Monitoring ICP also aids in clinical
situations beyond TBI, however the risk of neurosurgery
can prevent physicians from gathering the data. The
need for surgery may be eliminated if ICP could be
accurately inferred using noninvasive physiological
measures. Genetic programming (GP) and linear
regression were used to develop nonlinear and linear
mathematical models describing the relationships
between intercranial pressure and a collection of
physiological measurements from noninvasive
instruments. Nonlinear models of ICP were generated
that not only fit the subjects they were trained on,
but generalised well across other subjects. The
nonlinear models were analysed and provided insight
into the studied underlying system which led to the
creation of additional models. The new models were
developed with a refined search, and were more accurate
and general. It was also found that the relations
between the features could be explained effectively
with a simple linear model after GP refined the
search.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/CIBCB.2017.8058525",
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month = aug,
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notes = "Also known as \cite{8058525}",
- }
Genetic Programming entries for
James Alexander Hughes
Ethan Charles Jackson
Mark Daley
Citations