Machine Learning and AI for Healthcare

Big Data for Improved Health Outcomes

verfasst von: Arjun Panesar

Verlag: Apress

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges.

You’ll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization. You’ll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization.

Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things.

What You'll LearnGain a deeper understanding of key machine learning algorithms and their use and implementation within wider healthcare

Implement machine learning systems, such as speech recognition and enhanced deep learning/AI

Select learning methods/algorithms and tuning for use in healthcare

Recognize and prepare for the future of artificial intelligence in healthcare through best practices, feedback loops and intelligent agentsWho This Book Is For

Health care professionals interested in how machine learning can be used to develop health intelligence – with the aim of improving patient health, population health and facilitating significant care-payer cost savings.

Inhaltsverzeichnis

Frontmatter

Chapter 1. What Is Artificial Intelligence?

Abstract

Artificial intelligence (AI) is considered, once again, to be one of the most exciting advances of our time. Virtual assistants can determine our music tastes with remarkable accuracy, cars are now able to drive themselves, and mobile apps can reverse diseases once considered to be chronic and progressive.

Arjun Panesar

Chapter 2. Data

Abstract

Data is everywhere. Global disruption and international initiatives are driving datafication. Datafication refers to the modern-day trend of digitalizing (or datafying) every aspect of life. This data creation is enabling the transformation of data into new and potentially valuable forms. Entire municipalities are being incentivized to become smarter. In the not too distant future, our towns and cities will collect thousands of variables in real time to optimize, maintain, and enhance the quality of life for entire populations. One would reasonably expect that as well as managing traffic, traffic lights may also collect other data such as air quality, visibility, and speed of traffic. As a result of big data from connected devices, embedded sensors, and the IoT, there is a global need for the analysis, interpretation, and visualization of data.

Arjun Panesar

Chapter 3. What Is Machine Learning?

Abstract

The first Checkers program from machine learning pioneer Arthur Samuel debuted in 1956, demonstrating artificial “intelligence” capabilities. Since then, not only has the application of artificial intelligence grown, there is now a velocity, volume, and variety of data that has never been seen before. Samuel’s software ran on the IBM 701, a computer the size of a double bed. Data was typically discrete. Almost 70 years later, data is ubiquitous, and computers now more powerful such that 100 IBM 701s can fit into the palm of our hand. This has facilitated the subsets of machine learning and deep learning within AI (see Figure 3-1).

Arjun Panesar

Chapter 4. Machine Learning Algorithms

Abstract

You do not need a background in algebra and statistics to get started in machine learning. However, be under no illusions, mathematics is a huge part of machine learning. Math is key to understanding how the algorithm works and why coding a machine learning project from scratch is a great way to improve your mathematical and statistical skills. Not understanding the underlying principles behind an algorithm can lead to a limited understanding of methods or adopting limited interpretations of algorithms. If nothing else, it is useful to understand the mathematical principles that algorithms are based on and thus understand best which machine learning techniques are most appropriate.

Arjun Panesar

Chapter 5. Evaluating Learning for Intelligence

Abstract

As a field, machine learning is still in its infancy. Advanced machine learning has only been explored over the last 25 years, which has fueled data science as a profession. As a result, the data science industry is still in a phase of wonderment at the endless potential of AI and machine learning. With this comes both excitement and confusion—and an industry that is gathering knowledge, experience, and first-time problems.

Arjun Panesar

Chapter 6. Ethics of Intelligence

Abstract

From supermarket checkouts to airport check-ins and digital healthcare to Internet banking, the use of data and AI for decision-making is ubiquitous. There has been an astronomic growth in data availability over the last two decades, fueled by, first, connectivity, and now the Internet of Things. Traditional data science teams focus on the use of data for the creation, implementation, validation, and evaluation of machine learning models that can be for predictive analytics.

Arjun Panesar

Chapter 7. Future of Healthcare

Abstract

Connected healthcare and AI, when applied to healthcare, brings with it the ability to transform the lives of communities globally. People demand more today than ever before, and healthcare is expected to keep up in a golden era of innovation at the same time as a paradigm shift from healthcare volume to value—patient numbers to patient outcomes. As awareness of AI in healthcare grows, so too does public expectation that it will be used to improve day-to-day experiences.

Arjun Panesar

Chapter 8. Case Studies

Abstract

Artificial intelligence is improving the healthcare experience, bringing success to those who can leverage and adapt to a new health and care delivery paradigm. The proceeding case studies provide unique and engaging perspectives of the use of big data, AI, and machine learning within healthcare. Real-life descriptions of organizational approaches to data-identified healthcare problems demonstrates the instant value within available data.

Arjun Panesar

Backmatter

Titel: Machine Learning and AI for Healthcare
verfasst von: Arjun Panesar
Verlag: Apress
Electronic ISBN: 978-1-4842-3799-1
Print ISBN: 978-1-4842-3798-4
DOI: https://doi.org/10.1007/978-1-4842-3799-1