Pattern Recognition Techniques Applied to Biomedical Problems

Inbunden, Engelska, 2020

Del i serien STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health

557 kr

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Beskrivning

This book covers pattern recognition techniques applied to various areas of biomedicine, including disease diagnosis and prognosis, and several problems of classification, with a special focus on—but not limited to—pattern recognition modeling of biomedical signals and images. Multidisciplinary by definition, the book’s topic blends computing, mathematics and other technical sciences towards the development of computational tools and methodologies that can be applied to pattern recognition processes. In this work, the efficacy of such methods and techniques for processing medical information is analyzed and compared, and auxiliary criteria for determining the correct diagnosis and treatment strategies are recommended and applied. Researchers in applied mathematics, the computer sciences, engineering and related fields with a focus on medical applications will benefit from this book, as well as professionals witha special interest in state-of-the-art pattern recognition techniques as applied to biomedicine.

Produktinformation

Utgivningsdatum:2020-03-01
Mått:155 x 235 x 19 mm
Vikt:518 g
Format:Inbunden
Språk:Engelska
Serie:STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health
Antal sidor:221
Upplaga:20001
Förlag:Springer Nature Switzerland AG
ISBN:9783030380205

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Mer om författaren

Martha Refugio Ortiz-Posadas is a Professor at the Electrical Engineering Department, Universidad Autónoma Metropolitana, Iztapalapa, Mexico.

Innehållsförteckning

On the classification of independent components for biomedical signals de-noising: two study cases.- Pattern recognition applied to the analysis of genomic data and its association to diseases.- Image analysis method for the detection of Chagas parasite in blood image.- Monitoring and evaluating public health interventions.- Recognition of nausea patterns in multichannel electrogastrography.- Random Forest for Prediction of HIV Drug Resistance.- Analysis of cardiac contraction patterns.- Pattern Recognition of Chronic Patients Home Life and Out Patient Visits to Automate Follow up and Clinical Management.- Pattern recognition in medical equipment replacement at hospitals.

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