Big Data in Omics and Imaging: Integrated Analysis and Causal Inference addresses the recent development of integrated genomic, epigenomic and imaging data analysis and causal inference in big data era. Despite significant progress in dissecting the genetic architecture of complex diseases by genome-wide association studies (GWAS), genome-wide expression studies (GWES), and epigenome-wide association studies (EWAS), the overall contribution of the new identified genetic variants is small and a large fraction of genetic variants is still hidden. Understanding the etiology and causal chain of mechanism underlying complex diseases remains elusive. It is time to bring big data, machine learning and causal revolution to developing a new generation of genetic analysis for shifting the current paradigm of genetic analysis from shallow association analysis to deep causal inference and from genetic analysis alone to integrated omics and imaging data analysis for unraveling the mechanism of complex diseases. FEATURES Provides a natural extension and companion volume to Big Data in Omic and Imaging: Association Analysis, but can be read independently. Introduce causal inference theory to genomic, epigenomic and imaging data analysis Develop novel statistics for genome-wide causation studies and epigenome-wide causation studies. Bridge the gap between the traditional association analysis and modern causation analysis Use combinatorial optimization methods and various causal models as a general framework for inferring multilevel omic and image causal networks Present statistical methods and computational algorithms for searching causal paths from genetic variant to disease Develop causal machine learning methods integrating causal inference and machine learning Develop statistics for testing significant difference in directed edge, path, and graphs, and for assessing causal relationships between two networks The book is designed for graduate students and researchers in genomics, epigenomics, medical image, bioinformatics, and data science. Topics covered are: mathematical formulation of causal inference, information geometry for causal inference, topology group and Haar measure, additive noise models, distance correlation, multivariate causal inference and causal networks, dynamic causal networks, multivariate and functional structural equation models, mixed structural equation models, causal inference with confounders, integer programming, deep learning and differential equations for wearable computing, genetic analysis of function-valued traits, RNA-seq data analysis, causal networks for genetic methylation analysis, gene expression and methylation deconvolution, cell –specific causal networks, deep learning for image segmentation and image analysis, imaging and genomic data analysis, integrated multilevel causal genomic, epigenomic and imaging data analysis.

Master's Thesis from the year 2020 in the subject Health Sciences - Health Logistics, grade: 1,7, Linnaeus University (School of Informatics), course: Information Systems, language: English, abstract: This study was conducted to analyze this process closer focusing on a case of Cardiology. Conducting a comprehensive literature review and qualitative expert interviews, the impact of big data in the field of Cardiology was explored. The result of the study shows that big data can play a positive role in three aspects: prediction of disease, prevention of disease and management of disease. Big data enables us to build models that can be used to predict the occurrence of disease. Based on this information, actions can be taken to prevent the disease. Data also helps to manage the disease by offering helpful insights. Medical personnel can retrieve the patient data, with the help of AI, they can make faster decisions allowing them to spend more quality time with the patients and reduce cognitive errors. Through the interviews, it was understood that even though the positive role of big data has been acknowledged, the implementation is still a challenge due to various limitations. The challenges lie mainly on technical know-how and domain knowledge. Further challenges were data security and privacy issues that need to be addressed to mitigate the risks that can be caused by them. The examples of big data implementation in various cases like in heart failure prediction or prevention shows a positive picture. The overwhelming majority of case studies analyzed in this regard show an optimistic picture. Due to growing importance and use of smart devices, IoT, genomics and the recent developments in the field of ICTs, it is expected that big data will not only leave a positive influence on the field of Cardiology, it will also change the way medicine is practiced and healthcare is offered. The statement ‘Data is the new oil’ has been broadly acknowledged due to its wide-ranging importance. Utilizing big data offers a variety of benefits. Although the health sector was late in terms of exploiting the benefits of big data, currently, the adoption is accelerating. Healthcare is increasingly becoming an information science and the implementation of electronic medical records (EMR) and other information systems is growing rapidly. The patient data originating from smart devices and other sources like genomic databases are supporting the healthcare sector offering better healthcare delivery and increasing efficiency, hence saving costs.

Big Data in Omics and Imaging: Association Analysis addresses the recent development of association analysis and machine learning for both population and family genomic data in sequencing era. It is unique in that it presents both hypothesis testing and a data mining approach to holistically dissecting the genetic structure of complex traits and to designing efficient strategies for precision medicine. The general frameworks for association analysis and machine learning, developed in the text, can be applied to genomic, epigenomic and imaging data. FEATURES Bridges the gap between the traditional statistical methods and computational tools for small genetic and epigenetic data analysis and the modern advanced statistical methods for big data Provides tools for high dimensional data reduction Discusses searching algorithms for model and variable selection including randomization algorithms, Proximal methods and matrix subset selection Provides real-world examples and case studies Will have an accompanying website with R code The book is designed for graduate students and researchers in genomics, bioinformatics, and data science. It represents the paradigm shift of genetic studies of complex diseases– from shallow to deep genomic analysis, from low-dimensional to high dimensional, multivariate to functional data analysis with next-generation sequencing (NGS) data, and from homogeneous populations to heterogeneous population and pedigree data analysis. Topics covered are: advanced matrix theory, convex optimization algorithms, generalized low rank models, functional data analysis techniques, deep learning principle and machine learning methods for modern association, interaction, pathway and network analysis of rare and common variants, biomarker identification, disease risk and drug response prediction.

The field’s definitive text―updated with the latest advances in critical care and 1,000+ color images Comprehensive and current, Hall, Schmidt, and Wood's Principles of Critical Care is the authoritative guide to diagnosing and treating the most common problems encountered in the practice of critical care. Written by expert critical care physicians who are also experienced teachers, it features an organization, thoroughness, and clarity unavailable in other critical care resources. This peerless guide provides consensus on the complex and often-conflicting data in the practice of critical care, along with copious diagnostic and treatment algorithms. The text covers every aspect of critical care medicine essential to successful clinical practice, ranging from basic principles to the latest technologies. This updated fifth edition is highlighted by: In-depth, up-to-date descriptions of the unique presentation, differential diagnosis, and management of specific critical illnesses A logical organ system approach that simplifies the search for thorough and practical information necessary to manage a patient's specific condition New chapters on Oxygen Delivery Systems; Ultrasound in Critical Care; Fungal and Viral Infections; Pulmonary Hypertension; Alcohol Withdrawal; and COVID-19 and Hemophagocytic Lymphohistiocytosis (HLH) New material regarding critical care pandemic preparedness and response Enhanced cross-disciplinary chapters addressing the structures and systems of critical care, including staffing, safety, and informatics New contributions on caregiver and family issues and the implications of disordered sleep for the critically ill A full-color presentation

This reference provides a comprehensive overview of experimental and data analysis methodologies in vibrational spectroscopy and their biomedical applications. It focuses on the aspects of spectroscopic methods that are most relevant to biomedical applications INDICE: 1. Vibrational Spectroscopy in Microbiology and Medical Diagnostics (Dieter Naumann). 2. Biomedical Vibrational Spectroscopy - Technical Advances ... Etc.

Cardiology’s cornerstone text – thoroughly updated to reflect the latest clinical perspectives and cutting-edge topics Hailed for its authority, currency, and ability to translate the latest technical and clinical advances into clinical application, Hurst’s The Heart is the field’s landmark text and cardiology’s longest continuously published reference text. A previous edition of this trusted classic was described by Doody’s as “an outstanding choice for those who strive for a firm foundation in cardiovascular medicine, as well as an up-to-date and user-friendly source that addresses every discipline in the field.” Readers will find succinct, visually appealing summaries of all the major new trials, and guidelines, along with tips for optimizing outcomes and health quality. The Fourteenth Edition has been completely updated to reflect the latest technical, therapeutic, and clinical advances, while still maintaining a strong focus on patient care. Other enhancements include the addition of textual features such as Practice Points, Common Clinical Questions, and an increased number of the acclaimed Hurst’s diagrams. • NEW TO THIS EDITION: Section on Metabolic Disorders and Cardiovascular Disease, Cigarette Smoking and Cardiovascular Disease • Enhanced by more than 1,500 full-color illustrations and more than 500 tables • Brand new chapters include: Arrythmogenic Cardiomyopathy, Ischemic Mitral Regurgitation, Degenerative Mitral Valve Disease, Left Ventricular Noncompaction, Evaluation and Management of Acute Heart Failure, Carotid Artery Stenting, Race, Ethnicity, and Cardiovascular disease • The only comprehensive cardiology reference to publish every 2.5 years to allow for reporting of the latest trials and guidelines

Commercializing and exploiting applied Information and Communication Technologies (ICT) research results is critical in reducing the global Digital Divide and building a sustainable Knowledge Economy. This book brings together a comprehensive collection of over 210 in broad thematic areas.