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MATLAB Programming for Biomedical Engineers and Scientists, Second Edition provides an easy-to-learn introduction to the fundamentals of computer programming in MATLAB. The book explains the principles of good programming practice, while also demonstrating how to write efficient and robust code that analyzes and visualizes biomedical data. Aimed at the biomedical engineering student, biomedical scientist and medical researcher with little or no computer programming experience, this is an excellent resource for learning the principles and practice of computer programming using MATLAB. The book enables the reader to analyze problems and apply structured design methods to produce elegant, efficient and well-structured program designs, implement a structured program design in MATLAB, write code that makes good use of MATLAB programming features, including control structures, functions and advanced data types, and much more. - Presents many real-world biomedical problems and data, showing the practical application of programming concepts - Contains two whole chapters dedicated to the practicalities of designing and implementing more complex programs - Provides an accompanying website with freely available data and source code for the practical code examples, activities and exercises in the book - Includes new chapters on machine learning, engineering mathematics, and expanded coverage of data types
This book focuses on the role of computers in the provision of medical services. It provides both a conceptual framework and a practical approach for the implementation and management of IT used to improve the delivery of health care. Inspired by a Stanford University training program, it fills the need for a high quality text in computers and medicine. It meets the growing demand by practitioners, researchers, and students for a comprehensive introduction to key topics in the field. Completely revised and expanded, this work includes several new chapters filled with brand new material.
Winner of the Computer History Museum Prize of the Special Interest Group: Computers, Information, and Society Imagine biology and medicine today without computers. What would laboratory work be like if electronic databases and statistical software did not exist? Would disciplines like genomics even be feasible if we lacked the means to manage and manipulate huge volumes of digital data? How would patients fare in a world absent CT scans, programmable pacemakers, and computerized medical records? Today, computers are a critical component of almost all research in biology and medicine. Yet, just fifty years ago, the study of life was by far the least digitized field of science, its living subject matter thought too complex and dynamic to be meaningfully analyzed by logic-driven computers. In this long-overdue study, historian Joseph November explores the early attempts, in the 1950s and 1960s, to computerize biomedical research in the United States. Computers and biomedical research are now so intimately connected that it is difficult to imagine when such critical work was offline. Biomedical Computing transports readers back to such a time and investigates how computers first appeared in the research lab and doctor's office. November examines the conditions that made possible the computerization of biology—including strong technological, institutional, and political support from the National Institutes of Health—and shows not only how digital technology transformed the life sciences but also how the intersection of the two led to important developments in computer architecture and software design. The history of this phenomenon has been only vaguely understood. November's thoroughly researched and lively study makes clear for readers the motives behind computerizing the study of life and how that technology profoundly affects biomedical research today.
This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.
Over 900 references to monographic and journal literature about the use of computers in biology and medicine. Emphasis in the annotations is on computer applications, rather than on methods and results generally common to authors' abstracts. Entries arranged by topics under bibliographies, monographs, and articles. Author, subject indexes.