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 patientas 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.Ng V, Cardie C (2002) Improving machine learning approaches to coreference resolution. Proceedings of the ... Vilain M, Burger J, Aberdeen J, Connolly D, Hirschman L (1995) A model-theoretic coreference scoring scheme. Proceedings ofanbsp;...
|Title||:||Computational Medicine in Data Mining and Modeling|
|Author||:||Goran Rakocevic, Tijana Djukic, Nenad Filipovic, Veljko Milutinovic|
|Publisher||:||Springer Science & Business Media - 2013-10-17|