Название: Precision Health and Artificial Intelligence: With Privacy, Ethics, Bias, Health Equity, Best Practices, and Case Studies
Автор: Arjun Panesar
Издательство: Apress
Год: 2023
Страниц: 183
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB

This book provides a comprehensive explanation of precision (i.e., personalized) healthcare and explores how it can be advanced through Artificial Intelligence (AI) and other data-driven technologies.

From improving the diagnosis, treatment, and monitoring of many medical conditions to the effective implementation of precise patient care, this book will help you understand datasets produced from digital health technologies and IoT and teach you how to employ analytical methods such as convolutional neural networks and deep learning to analyze that data. You’ll also see how this data-driven approach can enhance and democratize value-based healthcare delivery. Additionally, you’ll learn how the convergence of AI and precision health is revolutionizing healthcare, including some of the most difficult challenges facing precision medicine, such as ethics, bias, privacy, and health equity.

Precision Health and Artificial Intelligence provides the groundwork for clinicians, engineers, bioinformaticians, and healthcare enthusiasts to apply AI to healthcare.

Artificial Intelligence refers to any cognitive ability exhibited by a nonhuman agent. The five fundamental components of Artificial Intelligence are learning, reasoning, problem-solving, perception, and language comprehension. AI has already surpassed human ability when it comes to performing tasks such as learning, vision, and logical reasoning. Machine Learning is a subset of AI where computer models are trained to learn from their actions and environment over time to improve. Algorithms adapt to the presentation of new data and discovery and, through Machine Learning, get iteratively better at tasks without having to be explicitly programmed to do so. Machine Learning is AI that can adapt autonomously with minimum human intervention.

Deep learning is a subset of Machine Learning that uses artificial neural networks to simulate the human brain’s learning process. The process is called deep learning due to additional layers that are added to a deep learning function when learning from data. Layers within the function are called neurons. When a deep learning model learns, it simply modifies weights, or neurons, using an optimization function.

Open-source toolkits support Machine Learning by providing accessible and ready-to-use code for common algorithms. Most are available for Python, the programming language favored for developing Machine Learning algorithms. Scikit-learn is a Python module containing image processing and Machine Learning techniques built on SciPy and enables algorithms for clustering, classification, and regression, such as naïve Bayes, decision trees, random forests, k-means, and support vector machines. NLTK, or Natural Language Toolkit, is a collection of libraries used in natural language processing (NLP). The NLTK enables the foundations for expert systems such as tokenization, stemming, tagging, parsing, and classification. Genism is a library for use on unstructured text, Scrapy provides open-source data mining, and TensorFlow is a popular Alphabet-backed open-source library of data computations optimized for Deep Learning. It enables multilayered neural networks and quick training. Keras and PyTorch are libraries for building Deep Learning algorithms with TensorFlow.

What You Will Learn
Understand the components required to facilitate precision health and personalized care
Apply and implement precision health systems
Overcome the challenges of delivering precision healthcare at scale
Reconcile ethical and moral implications of delivering precision healthcare
Gain insight into the hurdles providers face while implementing precision healthcare

Who This Book Is For
This book is a radically different alternative to books on precision health. It introduces healthcare professionals (nurses, physicians, practitioners, innovation officers), medical students, computing students, and data scientists to the use of data and AI in delivering precision healthcare and the critical considerations in its application without the need for endless code and mathematics. This book is a valuable source for clinicians, healthcare workers, and researchers from diverse areas of the biomedical field who may or may not have a computational background and want to learn more about the innovative field of artificial intelligence for precision health.