Main description:

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 Learn

Gain 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 agents

Who 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.

Contents:

Chapter 1: What is Artificial IntelligenceChapter Goal: Introduction to book and topics to be covered No of pages 10Sub -Topics1. What is AI, data science, machine and deep learning2. The case for learning from data3. Evolution of big data/learning/Analytics 3.04. Practical examples of how data can be used to learn within healthcare settings5. Conclusion
Chapter 2: DataChapter Goal: To understand data required for learning and how to ensure valid data for outcome veracityNo of pages: 30Sub - Topics 1. What is data, sources of data and what types of data is there? Little vs big data and the advantages/disadvantages with such data sets. Structured vs. unstructured data.2. The key aspects required of data, in particular, validity to ensure that only useful and relevant information3. How to use big data for learning (use cases)4. Turning data into information - how to collect data that can be used to improve health outcomes and examples of how to collect such data5. Challenges faced as part of the use of big data6. Data governance
Chapter 3: What is Machine learning?Chapter Goal: To introduce machine learning, identify/demystify types of learning and provide information of popular algorithms and their applicationsNo of pages: 45Sub - Topics: 1. Introduction - what is learning?2. Differences/similarities between: what is AI, data science, machine learning, deep learning3. History/evolution of learning4. Learning algorithms - popular types/categories, applications and their mathematical basis5. Software(s) used for learning
Chapter 4: Machine learning in healthcareChapter Goal: A comprehensive understanding of key concepts related to learning systems and the practical application of machine learning within healthcare settings No of pages: 50Sub - Topics: 1. Understanding Tasks, Performance and Experience to optimize algorithms and outcomes 2. Identification of algorithms to be used in healthcare applications for: predictive analysis, perspective analysis, inference, modeling, probability estimation, NLP etc and common uses3. Real-time analysis and analytics4. Machine learning best practices5. Neural networks, ANNs, deep learning
Chapter 5: Evaluating learning for intelligenceChapter Goal: To understand how to evaluate learning algorithms, how to choose the best evaluation technique/approach for analysisNo of pages: 101. How to evaluate machine learning systems 2. Methodologies for evaluating outputs3. Improving your intelligence4. Advanced analytics
Chapter 6: Ethics of intelligenceChapter Goal: To understand the hurdles that must be addressed in AI/machine learning and also overcome on both a micro- and macro-level to enable enhanced health intelligence No of pages: 251. The benefits of big data and machine learning2. The disadvantages of big data and machine learning - who owns the data, distributing the data, should patients/people be told what the results are (e.g. data demonstrates risk of cancer)3. Data for good, or data for bad?4. Topics that require addressing in order to ensure ease, efficiency and safety of outputs5. Do we need to govern our intelligence?
Chapter 7: The future of healthcareChapter Goal: Outline the direction of AI and machine/deep learning within healthcare and the future applications of intelligent systemsNo of pages: 301. Evidence-based medicine2. Patient data as the evidence base3. Healthcare disruption fueling innovation4. How generalisations on precise audiences enables personalized medicine5. Impact of data and IoT on realizing personalized medicine6. What about the ethics?7. Conclusion
Chapter 8: Case studiesChapter Goal: Real world applications of AI and machine/deep learning in healthcareNo of pages: 201. Real world case studies of organizations implementing machine learning and the challenges, methodologies, algorithms and analytics used to determine optimal performance/outcomes