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
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.
Health informatics students, practitioners, and researchers now have a complete resource specific to the profession. Health Informatics Research Methods: Principles and Practice supports seasoned and novice researchers, students, and educators. The text focuses on the practical applications of research in health informatics and health information management. It provides real-life examples of research with samples of survey instruments, step-by-step listings of methodology for several types of research designs, and examples of statistical analysis tables and explanations. The book's organization guides readers through the process of conducting research specific to health informatics concepts and functions.
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
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.
Health IT is a major field of investment in support of healthcare delivery, but patients and professionals tend to have systems imposed upon them by organizational policy or as a result of even higher policy decision. And, while many health IT systems are efficient and welcomed by their users, and are essential to modern healthcare, this is not the case for all. Unfortunately, some systems cause user frustration and result in inefficiency in use, and a few are known to have inconvenienced patients or even caused harm, including the occasional death. This book seeks to answer the need for better understanding of the importance of robust evidence to support health IT and to optimize investment in it; to give insight into health IT evidence and evaluation as its primary source; and to promote health informatics as an underpinning science demonstrating the same ethical rigour and proof of net benefit as is expected of other applied health technologies. The book is divided into three parts: the context and importance of evidence-based health informatics; methodological considerations of health IT evaluation as the source of evidence; and ensuring the relevance and application of evidence. A number of cross cutting themes emerge in each of these sections. This book seeks to inform the reader on the wide range of knowledge available, and the appropriateness of its use according to the circumstances. It is aimed at a wide readership and will be of interest to health policymakers, clinicians, health informaticians, the academic health informatics community, members of patient and policy organisations, and members of the vendor industry.
10.2 The Role and Contents of the URD in an Assessment Perspective -- 10.3 The Enterprise Model -- 10.4 The Normative Model -- 10.5 Assessment of the User Requirements Document -- 10.6 Discussion -- 11 Dynamic Aspects of the Assessment Methodology -- 11.1 Dynamic Aspects of IT-Development and Application -- 11.2 Adaptation of Frames of Reference for Assessment Activities -- 11.3 Feed-forward Loops -- 11.4 Support of Context Dependent Assessment -- 11.5 Conclusion -- 12 The Dynamic Assessment Methodology -- 12.1 Philosophy -- 12.2 Application Area -- 12.3 Operationalisation of the Methodology -- 12.4 Applicable Methods -- 12.5 Summary -- 13 Discussion -- 13.1 Discussion of Fulfilment of Objective for the 4th Goal -- 13.2 Conclusion of the Study -- References -- Appendix 1: Vocabulary -- Appendix 2: Abbreviations & Acronyms -- Appendix 3: KAVAS's & ISAR's Evaluation Methodology -- Appendix 4: Methodology for Assessment of Functionality -- Appendix 5: Experimental Observations: Functionality Assessment -- Appendix 6: Experimental Observations: LFA -- Appendix 7: Causal Analysis of Experimental Observations -- Appendix 8: Method for Elicitation of a Strategy -- Appendix 9: Selected References regarding Assessment Methods
This second edition of a pioneering technical work in biomedical informatics provides a very readable treatment of the deep computational ideas at the foundation of the field. Principles of Biomedical Informatics, 2nd Edition is radically reorganized to make it especially useable as a textbook for courses that move beyond the standard introductory material. It includes exercises at the end of each chapter, ideas for student projects, and a number of new topics, such as:• tree structured data, interval trees, and time-oriented medical data and their use• On Line Application Processing (OLAP), an old database idea that is only recently coming of age and finding surprising importance in biomedical informatics• a discussion of nursing knowledge and an example of encoding nursing advice in a rule-based system• X-ray physics and algorithms for cross-sectional medical image reconstruction, recognizing that this area was one of the most central to the origin of biomedical computing• an introduction to Markov processes, and• an outline of the elements of a hospital IT security program, focusing on fundamental ideas rather than specifics of system vulnerabilities or specific technologies. It is simultaneously a unified description of the core research concept areas of biomedical data and knowledge representation, biomedical information access, biomedical decision-making, and information and technology use in biomedical contexts, and a pre-eminent teaching reference for the growing number of healthcare and computing professionals embracing computation in health-related fields. As in the first edition, it includes many worked example programs in Common LISP, the most powerful and accessible modern language for advanced biomedical concept representation and manipulation. The text also includes humor, history, and anecdotal material to balance the mathematically and computationally intensive development in many of the topic areas. The emphasis, as in the first edition, is on ideas and methods that are likely to be of lasting value, not just the popular topics of the day. Ira Kalet is Professor Emeritus of Radiation Oncology, and of Biomedical Informatics and Medical Education, at the University of Washington. Until retiring in 2011 he was also an Adjunct Professor in Computer Science and Engineering, and Biological Structure. From 2005 to 2010 he served as IT Security Director for the University of Washington School of Medicine and its major teaching hospitals. He has been a member of the American Medical Informatics Association since 1990, and an elected Fellow of the American College of Medical Informatics since 2011. His research interests include simulation systems for design of radiation treatment for cancer, software development methodology, and artificial intelligence applications to medicine, particularly expert systems, ontologies and modeling. - Develops principles and methods for representing biomedical data, using information in context and in decision making, and accessing information to assist the medical community in using data to its full potential - Provides a series of principles for expressing biomedical data and ideas in a computable form to integrate biological, clinical, and public health applications - Includes a discussion of user interfaces, interactive graphics, and knowledge resources and reference material on programming languages to provide medical informatics programmers with the technical tools to develop systems
Provides a collection of medical IT research in topics such as clinical knowledge management, medical informatics, mobile health and service delivery, and gene expression.
