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Computational Medicine in Data Mining and Modeling [electronic resource] / edited by Goran Rakocevic, Tijana Djukic, Nenad Filipovic, Veljko Milutinović.

Contributor(s): Rakocevic, Goran [editor.] | Djukic, Tijana [editor.] | Filipovic, Nenad [editor.] | Milutinović, Veljko [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookPublisher: New York, NY : Springer New York : Imprint: Springer, 2013Description: X, 376 p. 171 illus., 128 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9781461487852.Subject(s): Computer science | Data mining | Artificial intelligence | Computer simulation | Bioinformatics | Computer Science | Data Mining and Knowledge Discovery | Computational Biology/Bioinformatics | Artificial Intelligence (incl. Robotics) | Simulation and ModelingAdditional physical formats: Printed edition:: No titleDDC classification: 006.312 Online resources: Click here to access online
Contents:
Mining Clinical Data -- Applications of probabilistic and related logics to decision support in medicine -- Transforming electronic medical books to diagnostic decision support systems using relational database management systems -- Text mining in medicine -- A primer on information theory, with applications to neuroscience -- Machine Learning based Imputation of Missing SNP Genotypes in SNP Genotype Arrays -- Computer modeling of atherosclerosis -- Particle dynamics and design of nano-drug delivery systems -- Computational Modeling of Ultrasound Wave Propagation in Bone.
In: Springer eBooksSummary: This book presents an overview of a variety of contemporary statistical, mathematical and computer science techniques which are used to further the knowledge in the medical domain. The authors focus on applying data mining to the medical domain, including mining the sets of clinical data typically found in patient's medical records, image mining, medical mining, data mining and machine learning applied to generic genomic data and more. This work also introduces modeling behavior of cancer cells, multi-scale computational models and simulations of blood flow through vessels by using patient-specific models. The authors cover different imaging techniques used to generate patient-specific models. This is used in computational fluid dynamics software to analyze fluid flow. Case studies are provided at the end of each chapter. Professionals and researchers with quantitative backgrounds will find Computational Medicine in Data Mining and Modeling useful as a reference. Advanced-level students studying computer science, mathematics, statistics and biomedicine will also find this book valuable as a reference or secondary text book.
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Mining Clinical Data -- Applications of probabilistic and related logics to decision support in medicine -- Transforming electronic medical books to diagnostic decision support systems using relational database management systems -- Text mining in medicine -- A primer on information theory, with applications to neuroscience -- Machine Learning based Imputation of Missing SNP Genotypes in SNP Genotype Arrays -- Computer modeling of atherosclerosis -- Particle dynamics and design of nano-drug delivery systems -- Computational Modeling of Ultrasound Wave Propagation in Bone.

This book presents an overview of a variety of contemporary statistical, mathematical and computer science techniques which are used to further the knowledge in the medical domain. The authors focus on applying data mining to the medical domain, including mining the sets of clinical data typically found in patient's medical records, image mining, medical mining, data mining and machine learning applied to generic genomic data and more. This work also introduces modeling behavior of cancer cells, multi-scale computational models and simulations of blood flow through vessels by using patient-specific models. The authors cover different imaging techniques used to generate patient-specific models. This is used in computational fluid dynamics software to analyze fluid flow. Case studies are provided at the end of each chapter. Professionals and researchers with quantitative backgrounds will find Computational Medicine in Data Mining and Modeling useful as a reference. Advanced-level students studying computer science, mathematics, statistics and biomedicine will also find this book valuable as a reference or secondary text book.

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