Andrew P. King – författare

Statistics for Biomedical Engineers and Scientists: How to Analyze and Visualize Data provides an intuitive understanding of the concepts of basic statistics, with a focus on solving biomedical problems. Readers will learn how to understand the fundamental concepts of descriptive and inferential statistics, analyze data and choose an appropriate hypothesis test to answer a given question, compute numerical statistical measures and perform hypothesis tests 'by hand', and visualize data and perform statistical analysis using MATLAB. Practical activities and exercises are provided, making this an ideal resource for students in biomedical engineering and the biomedical sciences who are in a course on basic statistics.

Presents a practical guide on how to visualize and analyze statistical data Provides numerous practical examples and exercises to illustrate the power of statistics in biomedical engineering applications Gives an intuitive understanding of statistical tests Covers practical skills by showing how to perform operations 'by hand' and by using MATLAB as a computational tool Includes an online resource with downloadable materials for students and teachers

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 provides a detailed technical overview of the use and applications of artificial intelligence (AI), machine learning and big data in cardiology. Recent technological advancements in these fields mean that there is significant gain to be had in applying these methodologies into day-to-day clinical practice. Chapters feature detailed technical reviews and highlight key current challenges and limitations, along with the available techniques to address them for each topic covered. Sample data sets are also included to provide hands-on tutorials for readers using Python-based Jupyter notebooks, and are based upon real-world examples to ensure the reader can develop their confidence in applying these techniques to solve everyday clinical problems.Artificial Intelligence and Big Data in Cardiology systematically describes and technically reviews the latest applications of AI and big data within cardiology. It is ideal for use by the trainee and practicing cardiologist andinformatician seeking an up-to-date resource on the topic with which to aid them in developing a thorough understanding of both basic concepts and recent advances in the field.

This book provides a detailed technical overview of the use and applications of artificial intelligence (AI), machine learning and big data in cardiology. Recent technological advancements in these fields mean that there is significant gain to be had in applying these methodologies into day-to-day clinical practice. Chapters feature detailed technical reviews and highlight key current challenges and limitations, along with the available techniques to address them for each topic covered. Sample data sets are also included to provide hands-on tutorials for readers using Python-based Jupyter notebooks, and are based upon real-world examples to ensure the reader can develop their confidence in applying these techniques to solve everyday clinical problems.Artificial Intelligence and Big Data in Cardiology systematically describes and technically reviews the latest applications of AI and big data within cardiology. It is ideal for use by the trainee and practicing cardiologist andinformatician seeking an up-to-date resource on the topic with which to aid them in developing a thorough understanding of both basic concepts and recent advances in the field.

This book constitutes the refereed proceedings of the 12th International Workshop on Clinical Image-Based Procedures, CLIP 2023, the First MICCAI Workshop on Fairness of AI in Medical Imaging, FAIMI 2023, and the Second MICCAI Workshop on the Ethical and Philosophical Issues in Medical Imaging, EPIMI 2023, held in conjunction with MICCAI 2023, in October 2023.CLIP 2023 accepted 5 full papers and 3 short papers form 8 submissions received. It focuses on holistic patient models for personalized healthcare with the goal to bring basic research methods closer to the clinical practice.For FAIMI 2023, 19 full papers have been accepted from 20 submissions. They focus on creating awareness about potential fairness issues that can emerge in the context of machine learning. And for EPIMI 2023, 2 papers have been accepted from 5 submissions. They investigate questions that underlie medical imaging research at the most fundamental level.