Download or read book Computational Medicine in Data Mining and Modeling written by Goran Rakocevic and published by Springer Science & Business Media. This book was released on 2013-10-17 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Predictive Modeling in Biomedical Data Mining and Analysis written by Sudipta Roy and published by Academic Press. This book was released on 2022-08-28 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: Predictive Modeling in Biomedical Data Mining and Analysis presents major technical advancements and research findings in the field of machine learning in biomedical image and data analysis. The book examines recent technologies and studies in preclinical and clinical practice in computational intelligence. The authors present leading-edge research in the science of processing, analyzing and utilizing all aspects of advanced computational machine learning in biomedical image and data analysis. As the application of machine learning is spreading to a variety of biomedical problems, including automatic image segmentation, image classification, disease classification, fundamental biological processes, and treatments, this is an ideal reference. Machine Learning techniques are used as predictive models for many types of applications, including biomedical applications. These techniques have shown impressive results across a variety of domains in biomedical engineering research. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood, hence the need for new resources and information. Includes predictive modeling algorithms for both Supervised Learning and Unsupervised Learning for medical diagnosis, data summarization and pattern identification Offers complete coverage of predictive modeling in biomedical applications, including data visualization, information retrieval, data mining, image pre-processing and segmentation, mathematical models and deep neural networks Provides readers with leading-edge coverage of biomedical data processing, including high dimension data, data reduction, clinical decision-making, deep machine learning in large data sets, multimodal, multi-task, and transfer learning, as well as machine learning with Internet of Biomedical Things applications
Download or read book Biomedical Data Mining for Information Retrieval written by Sujata Dash and published by John Wiley & Sons. This book was released on 2021-08-06 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: BIOMEDICAL DATA MINING FOR INFORMATION RETRIEVAL This book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications. Biomedical Data Mining for Information Retrieval comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and health informatics is an emerging field of research at the intersection of information science, computer science, and healthcare and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle. Previously, it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical image mining, a novel research area, due to the vast amount of available biomedical images, increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients’ biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions relating to healthcare. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients. Audience Researchers in various fields including computer science, medical informatics, healthcare IOT, artificial intelligence, machine learning, image processing, clinical big data analytics.
Download or read book Advanced Prognostic Predictive Modelling in Healthcare Data Analytics written by Sudipta Roy and published by Springer Nature. This book was released on 2021-04-22 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses major technical advancements and research findings in the field of prognostic modelling in healthcare image and data analysis. The use of prognostic modelling as predictive models to solve complex problems of data mining and analysis in health care is the feature of this book. The book examines the recent technologies and studies that reached the practical level and becoming available in preclinical and clinical practices in computational intelligence. The main areas of interest covered in this book are highest quality, original work that contributes to the basic science of processing, analysing and utilizing all aspects of advanced computational prognostic modelling in healthcare image and data analysis.
Download or read book Introduction to Computational Health Informatics written by Arvind Kumar Bansal and published by CRC Press. This book was released on 2020-01-08 with total page 664 pages. Available in PDF, EPUB and Kindle. Book excerpt: This class-tested textbook is designed for a semester-long graduate or senior undergraduate course on Computational Health Informatics. The focus of the book is on computational techniques that are widely used in health data analysis and health informatics and it integrates computer science and clinical perspectives. This book prepares computer science students for careers in computational health informatics and medical data analysis. Features Integrates computer science and clinical perspectives Describes various statistical and artificial intelligence techniques, including machine learning techniques such as clustering of temporal data, regression analysis, neural networks, HMM, decision trees, SVM, and data mining, all of which are techniques used widely used in health-data analysis Describes computational techniques such as multidimensional and multimedia data representation and retrieval, ontology, patient-data deidentification, temporal data analysis, heterogeneous databases, medical image analysis and transmission, biosignal analysis, pervasive healthcare, automated text-analysis, health-vocabulary knowledgebases and medical information-exchange Includes bioinformatics and pharmacokinetics techniques and their applications to vaccine and drug development
Download or read book Methods Models and Computation for Medical Informatics written by Gangopadhyay, Aryya and published by IGI Global. This book was released on 2012-12-31 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: Regular developments in technology continue to influence the medical and healthcare fields as they interact with information and computer sciences by methods of acquisition and the storage and retrieval of information. Methods, Models, and Computation for Medical Informatics is a comprehensive collection of research on computational capabilities, prototypes, and algorithms, as well as application in the areas of nursing, clinical care, public health, biomedical research, and much more. This book provides a better understanding of the models and methods used in the field of medicine for researchers, practitioners, and medical professionals alike.
- Author : Subhendu Kumar Pani
- Publisher : Springer Nature
- Release : 2021-12-13
- ISBN : 3030797538
- Pages : 416 pages
Assessing COVID 19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis
Download or read book Assessing COVID 19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis written by Subhendu Kumar Pani and published by Springer Nature. This book was released on 2021-12-13 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprehensively covers the topic of COVID-19 and other pandemics and epidemics data analytics using computational modelling. Biomedical and Health Informatics is an emerging field of research at the intersection of information science, computer science, and health care. The new era of pandemics and epidemics bring tremendous opportunities and challenges due to the plentiful and easily available medical data allowing for further analysis. The aim of pandemics and epidemics research is to ensure high-quality, efficient healthcare, better treatment and quality of life by efficiently analyzing the abundant medical, and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle. In the past, it was a common requirement to have domain experts for developing models for biomedical or healthcare. However, recent advances in representation learning algorithms allow us to automatically learn the pattern and representation of the given data for the development of such models. Medical Image Mining, a novel research area (due to its large amount of medical images) are increasingly generated and stored digitally. These images are mainly in the form of: computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients’ biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions related to health care. Image mining in medicine can help to uncover new relationships between data and reveal new and useful information that can be helpful for scientists and biomedical practitioners. Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis will play a vital role in improving human life in response to pandemics and epidemics. The state-of-the-art approaches for data mining-based medical and health related applications will be of great value to researchers and practitioners working in biomedical, health informatics, and artificial intelligence..
Download or read book Innovations in Data Methodologies and Computational Algorithms for Medical Applications written by Gangopadhyay, Aryya and published by IGI Global. This book was released on 2012-03-31 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Medicine has, until recently, been slow to adapt to information technologies and systems for many reasons, but the future lies therein.Innovations in Data Methodologies and Computational Algorithms for Medical Applications offers the most cutting-edge research in the field, offering insights into case studies and methodologies from around the world. The text details the latest developments and will serve as a vital resource to practitioners and academics alike in the burgeoning field of medical applications of technologies. As security and privacy improve, Electronic Health Records and informatics in the medical field are becoming ubiquitous, and staying abreast of the latest information can be difficult. This volume serves as a reference handbook and theoretical framework for the future of the field.
Download or read book Interactive Knowledge Discovery and Data Mining in Biomedical Informatics written by Andreas Holzinger and published by Springer. This book was released on 2014-06-17 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the grand challenges in our digital world are the large, complex and often weakly structured data sets, and massive amounts of unstructured information. This “big data” challenge is most evident in biomedical informatics: the trend towards precision medicine has resulted in an explosion in the amount of generated biomedical data sets. Despite the fact that human experts are very good at pattern recognition in dimensions of = 3; most of the data is high-dimensional, which makes manual analysis often impossible and neither the medical doctor nor the biomedical researcher can memorize all these facts. A synergistic combination of methodologies and approaches of two fields offer ideal conditions towards unraveling these problems: Human–Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the goal of supporting human capabilities with machine learning./ppThis state-of-the-art survey is an output of the HCI-KDD expert network and features 19 carefully selected and reviewed papers related to seven hot and promising research areas: Area 1: Data Integration, Data Pre-processing and Data Mapping; Area 2: Data Mining Algorithms; Area 3: Graph-based Data Mining; Area 4: Entropy-Based Data Mining; Area 5: Topological Data Mining; Area 6 Data Visualization and Area 7: Privacy, Data Protection, Safety and Security.
Download or read book Computational Medicine written by Zlatko Trajanoski and published by Springer Science & Business Media. This book was released on 2012-09-19 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational methodologies and modeling play a growing role for investigating mechanisms, and for the diagnosis and therapy of human diseases. This progress gave rise to computational medicine, an interdisciplinary field at the interface of computer science and medicine. The main focus of computational medicine lies in the development of data analysis methods and mathematical modeling as well as computational simulation techniques specifically addressing medical problems. In this book, we present a number of computational medicine topics at several scales: from molecules to cells, organs, and organisms. At the molecular level, tools for the analysis of genome variations as well as cloud computing resources for medical genetics are reviewed. Then, an analysis of gene expression data and the application to the characterization of microbial communities are highlighted. At the protein level, two types of analyses for mass spectrometry data are reviewed: labeled quantitative proteomics and lipidomics, followed by protein sequence analysis and a 3D structure and drug design chapter. Finally, three chapters on clinical applications focus on the integration of biomolecular and clinical data for cancer research, biomarker discovery, and network-based methods for computational diagnostics.
Download or read book Healthcare Data Analytics written by Chandan K. Reddy and published by CRC Press. This book was released on 2015-06-23 with total page 756 pages. Available in PDF, EPUB and Kindle. Book excerpt: At the intersection of computer science and healthcare, data analytics has emerged as a promising tool for solving problems across many healthcare-related disciplines. Supplying a comprehensive overview of recent healthcare analytics research, Healthcare Data Analytics provides a clear understanding of the analytical techniques currently available
Download or read book Data Mining in Biomedical Imaging Signaling and Systems written by Sumeet Dua and published by Auerbach Publications. This book was released on 2011-05-16 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 presented. Given the widespread deployment of complex biomedical systems, the authors discuss system-engineering principles in a proposal for a design of reliable systems. This comprehensive volume demonstrates the broad scope of uses for data mining and includes detailed strategies and methodologies for analyzing data from biomedical images, signals, and systems.
Download or read book Data Mining and Medical Knowledge Management Cases and Applications written by Berka, Petr and published by IGI Global. This book was released on 2009-02-28 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: The healthcare industry produces a constant flow of data, creating a need for deep analysis of databases through data mining tools and techniques resulting in expanded medical research, diagnosis, and treatment. Data Mining and Medical Knowledge Management: Cases and Applications presents case studies on applications of various modern data mining methods in several important areas of medicine, covering classical data mining methods, elaborated approaches related to mining in electroencephalogram and electrocardiogram data, and methods related to mining in genetic data. A premier resource for those involved in data mining and medical knowledge management, this book tackles ethical issues related to cost-sensitive learning in medicine and produces theoretical contributions concerning general problems of data, information, knowledge, and ontologies.
Download or read book Computational Modeling in Bioengineering and Bioinformatics written by Nenad Filipovic and published by Academic Press. This book was released on 2019-10-09 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Modeling in Bioengineering and Bioinformatics promotes complementary disciplines that hold great promise for the advancement of research and development in complex medical and biological systems, and in the environment, public health, drug design, and so on. It provides a common platform by bridging these two very important and complementary disciplines into an interactive and attractive forum. Chapters cover biomechanics and bioimaging, biomedical decision support system, data mining, personalized diagnoses, bio-signal processing, protein structure prediction, tissue and cell engineering, biomedical image processing, analysis and visualization, high performance computing and sports bioengineering. The book's chapters are the result of many international projects in the area of bioengineering and bioinformatics done at the Research and Development Center for Bioengineering BioIRC and by the Faculty of Engineering at the University of Kragujevac, Serbia. Presents recent advances at the crossroads of biomedical engineering and bioinformatics, one of the hottest areas in biomedical and clinical research Discusses a wide range of leading-edge research topics, including biomechanics and bioimaging, biomedical decision support systems, data mining, personalized diagnoses, bio-signal processing, protein structure prediction, tissue and cell engineering, amongst others Includes coverage of biomechanical, bioengineering and computational methods of treatment and diagnosis
Download or read book Contemporary Experimental Design Multivariate Analysis and Data Mining written by Jianqing Fan and published by Springer Nature. This book was released on 2020-05-22 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: The collection and analysis of data play an important role in many fields of science and technology, such as computational biology, quantitative finance, information engineering, machine learning, neuroscience, medicine, and the social sciences. Especially in the era of big data, researchers can easily collect data characterised by massive dimensions and complexity. In celebration of Professor Kai-Tai Fang’s 80th birthday, we present this book, which furthers new and exciting developments in modern statistical theories, methods and applications. The book features four review papers on Professor Fang’s numerous contributions to the fields of experimental design, multivariate analysis, data mining and education. It also contains twenty research articles contributed by prominent and active figures in their fields. The articles cover a wide range of important topics such as experimental design, multivariate analysis, data mining, hypothesis testing and statistical models.
Download or read book Computational Modeling in Biomedical Engineering and Medical Physics written by Alexandru Morega and published by Academic Press. This book was released on 2020-10-02 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical and numerical modelling of engineering problems in medicine is aimed at unveiling and understanding multidisciplinary interactions and processes and providing insights useful to clinical care and technology advances for better medical equipment and systems. When modelling medical problems, the engineer is confronted with multidisciplinary problems of electromagnetism, heat and mass transfer, and structural mechanics with, possibly, different time and space scales, which may raise concerns in formulating consistent, solvable mathematical models. Computational Medical Engineering presents a number of engineering for medicine problems that may be encountered in medical physics, procedures, diagnosis and monitoring techniques, including electrical activity of the heart, hemodynamic activity monitoring, magnetic drug targeting, bioheat models and thermography, RF and microwave hyperthermia, ablation, EMF dosimetry, and bioimpedance methods. The authors discuss the core approach methodology to pose and solve different problems of medical engineering, including essentials of mathematical modelling (e.g., criteria for well-posed problems); physics scaling (homogenization techniques); Constructal Law criteria in morphing shape and structure of systems with internal flows; computational domain construction (CAD and, or reconstruction techniques based on medical images); numerical modelling issues, and validation techniques used to ascertain numerical simulation results. In addition, new ideas and venues to investigate and understand finer scale models and merge them into continuous media medical physics are provided as case studies. Presents the fundamentals of mathematical and numerical modeling of engineering problems in medicine Discusses many of the most common modelling scenarios for Biomedical Engineering, including, electrical activity of the heart hemodynamic activity monitoring, magnetic drug targeting, bioheat models and thermography, RF and microwave hyperthermia, ablation, EMF dosimetry, and bioimpedance methods Includes discussion of the core approach methodology to pose and solve different problems of medical engineering, including essentials of mathematical modelling, physics scaling, Constructal Law criteria in morphing shape and structure of systems with internal flows, computational domain construction, numerical modelling issues, and validation techniques used to ascertain numerical simulation results
Download or read book Signal Processing in Medicine and Biology written by Iyad Obeid and published by Springer Nature. This book was released on 2020-03-16 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers emerging trends in signal processing research and biomedical engineering, exploring the ways in which signal processing plays a vital role in applications ranging from medical electronics to data mining of electronic medical records. Topics covered include statistical modeling of electroencephalograph data for predicting or detecting seizure, stroke, or Parkinson’s; machine learning methods and their application to biomedical problems, which is often poorly understood, even within the scientific community; signal analysis; medical imaging; and machine learning, data mining, and classification. The book features tutorials and examples of successful applications that will appeal to a wide range of professionals and researchers interested in applications of signal processing, medicine, and biology.