Methods In Biomedical Informatics

Beginning with a survey of fundamental concepts associated with data integration, knowledge representation, and hypothesis generation from heterogeneous data sets, Methods in Biomedical Informatics provides a practical survey of methodologies used in biological, clinical, and public health contexts. These concepts provide the foundation for more advanced topics like information retrieval, natural language processing, Bayesian modeling, and learning classifier systems. The survey of topics then concludes with an exposition of essential methods associated with engineering, personalized medicine, and linking of genomic and clinical data. Within an overall context of the scientific method, Methods in Biomedical Informatics provides a practical coverage of topics that is specifically designed for: (1) domain experts seeking an understanding of biomedical informatics approaches for addressing specific methodological needs; or (2) biomedical informaticians seeking an approachable overview of methodologies that can be used in scenarios germane to biomedical research. Contributors represent leading biomedical informatics experts: individuals who have demonstrated effective use of biomedical informatics methodologies in the real-world, high-quality biomedical applications Material is presented as a balance between foundational coverage of core topics in biomedical informatics with practical "in-the-trenches" scenarios. Contains appendices that function as primers on: (1) Unix; (2) Ruby; (3) Databases; and (4) Web Services.

As director of a training program in medical informatics, I have found that one of the most frequent inquiries from graduate students is, "Although I am happy with my research focus and the work I have done, how can I design and carry out a practical evaluation that proves the value of my contribution?" Informatics is a multifaceted, interdisciplinary field with research that ranges from theoretical developments to projects that are highly applied and intended for near-term use in clinical settings. The implications of "proving" a research claim accordingly vary greatly depending on the details of an individual student's goals and thesis state ment. Furthermore, the dissertation work leading up to an evaluation plan is often so time-consuming and arduous that attempting the "perfect" evaluation is fre quently seen as impractical or as diverting students from central programming or implementation issues that are their primary areas of interest. They often ask what compromises are possible so they can provide persuasive data in support of their claims without adding another two to three years to their graduate student life. Our students clearly needed help in dealing more effectively with such dilem mas, and it was therefore fortuitous when, in the autumn of 1991, we welcomed two superb visiting professors to our laboratories.

Deep Learning Techniques for Biomedical and Health Informatics provides readers with the state-of-the-art in deep learning-based methods for biomedical and health informatics. The book covers not only the best-performing methods, it also presents implementation methods. The book includes all the prerequisite methodologies in each chapter so that new researchers and practitioners will find it very useful. Chapters go from basic methodology to advanced methods, including detailed descriptions of proposed approaches and comprehensive critical discussions on experimental results and how they are applied to Biomedical Engineering, Electronic Health Records, and medical image processing. Examines a wide range of Deep Learning applications for Biomedical Engineering and Health Informatics, including Deep Learning for drug discovery, clinical decision support systems, disease diagnosis, prediction and monitoring Discusses Deep Learning applied to Electronic Health Records (EHR), including health data structures and management, deep patient similarity learning, natural language processing, and how to improve clinical decision-making Provides detailed coverage of Deep Learning for medical image processing, including optimizing medical big data, brain image analysis, brain tumor segmentation in MRI imaging, and the future of biomedical image analysis

The Handbook of Evaluation Methods for Health Informatics provides a complete compendium of methods for evaluation of IT-based systems and solutions within healthcare. Emphasis is entirely on assessment of the IT-system within its organizational environment. The author provides a coherent and complete assessment of methods addressing interactions with and effects of technology at the organizational, psychological, and social levels. It offers an explanation of the terminology and theoretical foundations underlying the methodological analysis presented here. The author carefully guides the reader through the process of identifying relevant methods corresponding to specific information needs and conditions for carrying out the evaluation study. The Handbook takes a critical view by focusing on assumptions for application, tacit built-in perspectives of the methods as well as their perils and pitfalls. Collects a number of evaluation methods of medical informatics Addresses metrics and measures Includes an extensive list of anotated references, case studies, and a list of useful Web sites

This book provides a comprehensive overview of different biomedical data types, including both clinical and genomic data. Thorough explanations enable readers to explore key topics ranging from electrocardiograms to Big Data health mining and EEG analysis techniques. Each chapter offers a summary of the field and a sample analysis. Also covered are telehealth infrastructure, healthcare information association rules, methods for mass spectrometry imaging, environmental biodiversity, and the global nonlinear fitness function for protein structures. Diseases are addressed in chapters on functional annotation of lncRNAs in human disease, metabolomics characterization of human diseases, disease risk factors using SNP data and Bayesian methods, and imaging informatics for diagnostic imaging marker selection. With the exploding accumulation of Electronic Health Records (EHRs), there is an urgent need for computer-aided analysis of heterogeneous biomedical datasets. Biomedical data is notorious for its diversified scales, dimensions, and volumes, and requires interdisciplinary technologies for visual illustration and digital characterization. Various computer programs and servers have been developed for these purposes by both theoreticians and engineers. This book is an essential reference for investigating the tools available for analyzing heterogeneous biomedical data. It is designed for professionals, researchers, and practitioners in biomedical engineering, diagnostics, medical electronics, and related industries.

Heavily updated and revised from the successful first edition Appeals to a wide range of informatics professionals, from students to on-site medical information system administrators Includes case studies and real world system evaluations References and self-tests for feedback and motivation after each chapter Great for teaching purposes, the book is recommended for courses offered at universities such as Columbia University Precise definition and use of terms

Too often, healthcare workers are led to believe that medical informatics is a complex field that can only be mastered by teams of professional programmers. This is simply not the case. With just a few dozen simple algorithms, easily implemented with open source programming languages, you can fully utilize the medical information contained in clini

Heavily updated and revised from the successful first edition Appeals to a wide range of informatics professionals, from students to on-site medical information system administrators Includes case studies and real world system evaluations References and self-tests for feedback and motivation after each chapter Great for teaching purposes, the book is recommended for courses offered at universities such as Columbia University Precise definition and use of terms