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Books > Medicine > Other branches of medicine > Medical imaging
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Data Mining in Biomedical Imaging, Signaling, and Systems (Paperback)
Loot Price: R1,876
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Data Mining in Biomedical Imaging, Signaling, and Systems (Paperback)
Expected to ship within 10 - 15 working days
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Data mining can help pinpoint hidden information in medical data
and accurately differentiate pathological from normal data. It can
help to extract hidden features from patient groups and disease
states and can aid in automated decision making. Data Mining in
Biomedical Imaging, Signaling, and Systems provides an in-depth
examination of the biomedical and clinical applications of data
mining. It supplies examples of frequently encountered
heterogeneous data modalities and details the applicability of data
mining approaches used to address the computational challenges in
analyzing complex data. The book details feature extraction
techniques and covers several critical feature descriptors. As
machine learning is employed in many diagnostic applications, it
covers the fundamentals, evaluation measures, and challenges of
supervised and unsupervised learning methods. Both feature
extraction and supervised learning are discussed as they apply to
seizure-related patterns in epilepsy patients. Other specific
disorders are also examined with regard to the value of data mining
for refining clinical diagnoses, including depression and recurring
migraines. The diagnosis and grading of the world's fourth most
serious health threat, depression, and analysis of acoustic
properties that can distinguish depressed speech from normal are
also described. Although a migraine is a complex neurological
disorder, the text demonstrates how metabonomics can be effectively
applied to clinical practice. The authors review alignment-based
clustering approaches, techniques for automatic analysis of biofilm
images, and applications of medical text mining, including text
classification applied to medical reports. The identification and
classification of two life-threatening heart abnormalities,
arrhythmia and ischemia, are addressed, and a unique segmentation
method for mining a 3-D imaging biomarker, exemplified by
evaluation of osteoarthritis, is also present
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