Practical AI for Healthcare Professionals: Machine Learning with Numpy, Scikit-l

Description: Practical AI for Healthcare Professionals by Abhinav Suri Practical AI for Healthcare Professionals Artificial Intelligence (AI) is a buzzword in the healthcare sphere today. FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher Description Practical AI for Healthcare Professionals Artificial Intelligence (AI) is a buzzword in the healthcare sphere today. However, notions of what AI actually is and how it works are often not discussed. Furthermore, information on AI implementation is often tailored towards seasoned programmers rather than the healthcare professional/beginner coder. This book gives an introduction to practical AI in the medical sphere, focusing on real-life clinical problems, how to solve them with actual code, and how to evaluate the efficacy of those solutions. Youll start by learning how to diagnose problems as ones that can and cannot be solved with AI. Youll then learn the basics of computer science algorithms, neural networks, and when each should be applied. Then youll tackle the essential parts of basic Python programming relevant to data processing and making AI programs. The Tensorflow/Keras library along with Numpy and Scikit-Learn are covered as well. Once youve mastered those basic computer science and programming concepts, you can dive into projects with code, implementation details, and explanations. These projects give you the chance to explore using machine learning algorithms for issues such as predicting the probability of hospital admission from emergency room triage and patient demographic data. We will then use deep learning to determine whether patients have pneumonia using chest X-Ray images. The topics covered in this book not only encompass areas of the medical field where AI is already playing a major role, but also are engineered to cover as much as possible of AI that is relevant to medical diagnostics. Along the way, readers can expect to learn data processing, how to conceptualize problems that can be solved by AI, and how to program solutions to those problems. Physicians and other healthcare professionals who can master these skills will be able to lead AI-based research and diagnostic tool development, ultimately benefiting countless patients. Back Cover Use Artificial Intelligence (AI) to analyze and diagnose what previously could only be handled by trained medical professionals. This book gives an introduction to practical AI, focusing on real-life medical problems, how to solve them with actual code, and how to evaluate the efficacy of these solutions. Youll start by learning how to diagnose problems as ones that can and cannot be solved with AI or computer science algorithms. If youre not familiar with those algorithms, thats not a problem. Youll learn the basics of algorithms and neural networks and when each should be applied. Then youll tackle the essential parts of basic Python programming relevant to data processing and making AI programs. The TensorFlow library alogn with Numpy and Scikit-Learn are covered, too. Once youve mastered those basic computer science concepts, you can dive into three projects with code, implementation details and explanation, and diagnostic utility analysis. These projects give you the change to explore using machine learning algorithms for diagnosing diabetes from patient data, using basic neural networks for heart disease prediction from cardiac data, and using convolutional networks for brain tumor segmentation from MRI scans The topics and projects covered not only encompass areas of the medical field where AI is already playing a major role but also are engineered to cover as much as possible of AI that is relevant to medical diagnostics. Along the way, readers can expect to learn data processing, how to conceptualize problems that can be solved by AI, and how to program solutions to problems using modern libraries, such as TensorFlow. Physicians and other healthcare professionals who can master these skills will be able to lead AI-based research and diagnostic tool development, ultimately benefiting countless patients. Author Biography Abhinav "Abhi" Suri is a current medical student at the UCLA David Geffen School of Medicine. He completed his undergraduate degree at the University of Pennsylvania with majors in Computer Science and Biology. He also completed a Masters in Public Health (in Epidemiology) at Columbia University Mailman School of Public Health. Abhihas been dedicated to exploring the intersection between computer science and medicine. As an undergraduate, he carried out and directed research on deep learning algorithms for the detection of vertebral deformities and the detection of genetic factors that increase risk of COPD. His public health research focused on opioid usage trends in NY State and the development/utilization of geospatial dashboards for monitoring demographic disease trends in the COVID-19 pandemic. Outside of classes and research, Abhi is an avid programmer and has made applications that address healthcare worker access in Tanzania, aid the discovery process foranti-wage theft cases, and facilitate access to arts classes in underfunded school districts. He also developed (and currently maintains) a popular open-source repository, Flask-Base, which has over 2,000 stars on Github. He also enjoys teaching (lectured a course on JavaScript) and writing. So far, his authored articles and videos have reached over 200,000 people across a variety of platforms. Table of Contents Chapter 1: Introduction to AI and its Use Cases- Chapter 2: Computational Thinking.- Chapter 3: Overview of Programming.- Chapter 4: A Brief Tour of Machine Learning Algorithms. -Chapter 5: Project #1 Neural Networks & Heart Disease.- Chapter 6: Project #2 CNNs & Brain Tumor Detection.- Chapter 7: The Future of Healthcare and AI. Feature Code and conceptualize practical AI projects for healthcare diagnosis of diabetes, heart disease, and brain cancer Improve the lives of patients by developing new AI tooling even without a background in advanced software engineering Push the boundaries of diagnosis with innovative AI-solutions Details ISBN1484277791 Author Abhinav Suri Short Title Practical AI for Healthcare Professionals Language English ISBN-10 1484277791 ISBN-13 9781484277799 Format Paperback Publisher APress Year 2021 Imprint APress Place of Publication Berkley Country of Publication United States Edition 1st Pages 254 Publication Date 2021-12-14 AU Release Date 2021-12-14 NZ Release Date 2021-12-14 US Release Date 2021-12-14 UK Release Date 2021-12-14 Subtitle Machine Learning with Numpy, Scikit-learn, and TensorFlow Illustrations 45 Illustrations, black and white; XIV, 254 p. 45 illus. Edition Description 1st ed. 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