Machine Learning and AI for Healthcare

Big Data for Improved Health Outcomes

This updated second edition offers a guided tour of machine learning algorithms and architecture design. It provides real-world applications of intelligent systems in healthcare and covers the challenges of managing big data. Les mer
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Paperback
Legg i
Paperback
Legg i
Vår pris: 445,-

(Paperback) Fri frakt!
Leveringstid: Sendes innen 7 virkedager
På grunn av Brexit-tilpasninger og tiltak for å begrense covid-19 kan det dessverre oppstå forsinket levering.

Om boka

This updated second edition offers a guided tour of machine learning algorithms and architecture design. It provides real-world applications of intelligent systems in healthcare and covers the challenges of managing big data.

The book has been updated with the latest research in massive data, machine learning, and AI ethics. It covers new topics in managing the complexities of massive data, and provides examples of complex machine learning models. Updated case studies from global healthcare providers showcase the use of big data and AI in the fight against chronic and novel diseases, including COVID-19. The ethical implications of digital healthcare, analytics, and the future of AI in population health management are explored. You will learn how to create a machine learning model, evaluate its performance, and operationalize its outcomes within your organization. Case studies from leading healthcare providers cover scaling global digital services. Techniques are presented to evaluate the efficacy, suitability, and efficiency of AI machine learning applications through case studies and best practice, including the Internet of Things.


You will understand 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.





What You Will Learn






Understand key machine learning algorithms and their use and implementation within healthcare
Implement machine learning systems, such as speech recognition and enhanced deep learning/AI
Manage the complexities of massive data
Be familiar with AI and healthcare 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.

Fakta

Innholdsfortegnelse

Chapter 1: Introduction: Learning for Healthcare Chapter 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: Big Data Chapter Goal: To understand data required for learning and how to ensure valid data for outcome veracityNo of pages: 35Sub - 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. Massive data - management and complexities3. The key aspects required of data, in particular, validity to ensure that only useful and relevant information4. How to use big data for learning (use cases)5. Turning data into information - how to collect data that can be used to improve health outcomes and examples of how to collect such data6. Challenges faced as part of the use of big data7. 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, complex examples of machine learning models, applications and their mathematical basis5. Software(s) used for learning6. Code samples
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

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