Zum Hauptinhalt springen
Dekorationsartikel gehören nicht zum Leistungsumfang.
Machine Learning and AI for Healthcare
Big Data for Improved Health Outcomes
Taschenbuch von Arjun Panesar
Sprache: Englisch

67,90 €*

inkl. MwSt.

Versandkostenfrei per Post / DHL

Lieferzeit 1-2 Wochen

Kategorien:
Beschreibung
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.
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.
Über den Autor

Arjun Panesar is the founder of Diabetes Digital Media (DDM), the world's largest diabetes community and provider of evidence-based digital health interventions. He holds an honors degree (MEng) in computing and artificial intelligence from Imperial College, London. He has a decade of experience in big data and affecting user outcomes, and leads the development of intelligent, evidence-based digital health interventions that harness the power of big data and machine learning to provide precision patient care to patients, health agencies, and governments worldwide.

Arjun's work has received international recognition and was featured by the BBC, Forbes, New Scientist, and The Times. He has received innovation, business, and technology awards, including being named the top app for prevention of type 2 diabetes.

Arjun is an advisor to the Information School, at the University of Sheffield, Fellow to the NHS Innovation Accelerator, and was recognized by Imperial College as an Emerging Leader in 2020 for his contribution and impact to society.

Zusammenfassung

Offers healthcare professionals a tech jargon-free understanding of the applications of machine learning in healthcare

Covers the ethics of data and learning governance and the hurdles that require addressing to achieve a long-term gain from machine learning and AI

Written by an award-winning researcher of intelligent systems that improve user experience through collaboration, machine learning, and data mining

Inhaltsverzeichnis
Chapter 1: Introduction: Learning for Healthcare
Chapter Goal: Introduction to book and topics to be covered
No of pages 10
Sub -Topics
1. What is AI, data science, machine and deep learning
2. The case for learning from data
3. Evolution of big data/learning/Analytics 3.0
4. Practical examples of how data can be used to learn within healthcare settings
5. Conclusion
Chapter 2: Big Data
Chapter Goal: To understand data required for learning and how to ensure valid data for outcome veracity
No of pages: 35
Sub - 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 complexities
3. The key aspects required of data, in particular, validity to ensure that only useful and relevant information
4. 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 data
6. Challenges faced as part of the use of big data
7. 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 applications
No of pages: 45
Sub - Topics:
1. Introduction - what is learning?
2. Differences/similarities between: what is AI, data science, machine learning, deep learning
3. History/evolution of learning
4. Learning algorithms - popular types/categories, complex examples of machine learning models, applications and their mathematical basis
5. Software(s) used for learning
6. Code samples

Chapter 4: Machine Learning in Healthcare
Chapter Goal: A comprehensive understanding of key concepts related to learning systems and the practical application of machine learning within healthcare settings
No of pages: 50
Sub - 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 uses
3. Real-time analysis and analytics
4. Machine learning best practices
5. Neural networks, ANNs, deep learning
6. Code samples
Chapter 5: Evaluating Learning for Intelligence
Chapter Goal: To understand how to evaluate learning algorithms, how to choose the best evaluation technique/approach for analysis
No of pages: 30
1. How to evaluate machine learning systems
2. Methodologies for evaluating outputs
3. Improving your intelligence
4. Advanced analytics
5. Real-world examples of evaluations
Chapter 6: Ethics of intelligence
Chapter 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: 25
1. The benefits of big data and machine learning
2. 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 outputs
5. Do we need to govern our intelligence?
6. Example: COVID-19 response and data/privacy sharing
Chapter 7: The Future of Healthcare
Chapter Goal: Outline the direction of AI and machine/deep learning within healthcare and the future applications of intelligent systems
No of pages: 30
1. Evidence-based medicine
2. Patient data as the evidence base
3. Healthcare disruption fueling innovation
4. How generalisations on precise audiences enables personalized medicine
5. Impact of data and IoT on realizing personalized medicine
6. AI ethics
7. Conclusion
Chapter 8: Case studies
Chapter Goal: Real world applications of AI and machine/deep learning in healthcare
No of pages: 50
1. Real world case studies of organizations implementing machine learning and the challenges, methodologies, algorithms and analytics used to determine optimal performance/outcomes
2. COVID-related case studies: how data was used, how rapid interventions were deployed, agile development methodolodies
Details
Erscheinungsjahr: 2020
Genre: Importe, Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Inhalt: xxx
407 S.
61 s/w Illustr.
407 p. 61 illus.
ISBN-13: 9781484265369
ISBN-10: 148426536X
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Panesar, Arjun
Auflage: 2nd ed.
Hersteller: Apress
Apress L.P.
Verantwortliche Person für die EU: Books on Demand GmbH, In de Tarpen 42, D-22848 Norderstedt, info@bod.de
Maße: 254 x 178 x 24 mm
Von/Mit: Arjun Panesar
Erscheinungsdatum: 16.12.2020
Gewicht: 0,822 kg
Artikel-ID: 118951081
Über den Autor

Arjun Panesar is the founder of Diabetes Digital Media (DDM), the world's largest diabetes community and provider of evidence-based digital health interventions. He holds an honors degree (MEng) in computing and artificial intelligence from Imperial College, London. He has a decade of experience in big data and affecting user outcomes, and leads the development of intelligent, evidence-based digital health interventions that harness the power of big data and machine learning to provide precision patient care to patients, health agencies, and governments worldwide.

Arjun's work has received international recognition and was featured by the BBC, Forbes, New Scientist, and The Times. He has received innovation, business, and technology awards, including being named the top app for prevention of type 2 diabetes.

Arjun is an advisor to the Information School, at the University of Sheffield, Fellow to the NHS Innovation Accelerator, and was recognized by Imperial College as an Emerging Leader in 2020 for his contribution and impact to society.

Zusammenfassung

Offers healthcare professionals a tech jargon-free understanding of the applications of machine learning in healthcare

Covers the ethics of data and learning governance and the hurdles that require addressing to achieve a long-term gain from machine learning and AI

Written by an award-winning researcher of intelligent systems that improve user experience through collaboration, machine learning, and data mining

Inhaltsverzeichnis
Chapter 1: Introduction: Learning for Healthcare
Chapter Goal: Introduction to book and topics to be covered
No of pages 10
Sub -Topics
1. What is AI, data science, machine and deep learning
2. The case for learning from data
3. Evolution of big data/learning/Analytics 3.0
4. Practical examples of how data can be used to learn within healthcare settings
5. Conclusion
Chapter 2: Big Data
Chapter Goal: To understand data required for learning and how to ensure valid data for outcome veracity
No of pages: 35
Sub - 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 complexities
3. The key aspects required of data, in particular, validity to ensure that only useful and relevant information
4. 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 data
6. Challenges faced as part of the use of big data
7. 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 applications
No of pages: 45
Sub - Topics:
1. Introduction - what is learning?
2. Differences/similarities between: what is AI, data science, machine learning, deep learning
3. History/evolution of learning
4. Learning algorithms - popular types/categories, complex examples of machine learning models, applications and their mathematical basis
5. Software(s) used for learning
6. Code samples

Chapter 4: Machine Learning in Healthcare
Chapter Goal: A comprehensive understanding of key concepts related to learning systems and the practical application of machine learning within healthcare settings
No of pages: 50
Sub - 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 uses
3. Real-time analysis and analytics
4. Machine learning best practices
5. Neural networks, ANNs, deep learning
6. Code samples
Chapter 5: Evaluating Learning for Intelligence
Chapter Goal: To understand how to evaluate learning algorithms, how to choose the best evaluation technique/approach for analysis
No of pages: 30
1. How to evaluate machine learning systems
2. Methodologies for evaluating outputs
3. Improving your intelligence
4. Advanced analytics
5. Real-world examples of evaluations
Chapter 6: Ethics of intelligence
Chapter 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: 25
1. The benefits of big data and machine learning
2. 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 outputs
5. Do we need to govern our intelligence?
6. Example: COVID-19 response and data/privacy sharing
Chapter 7: The Future of Healthcare
Chapter Goal: Outline the direction of AI and machine/deep learning within healthcare and the future applications of intelligent systems
No of pages: 30
1. Evidence-based medicine
2. Patient data as the evidence base
3. Healthcare disruption fueling innovation
4. How generalisations on precise audiences enables personalized medicine
5. Impact of data and IoT on realizing personalized medicine
6. AI ethics
7. Conclusion
Chapter 8: Case studies
Chapter Goal: Real world applications of AI and machine/deep learning in healthcare
No of pages: 50
1. Real world case studies of organizations implementing machine learning and the challenges, methodologies, algorithms and analytics used to determine optimal performance/outcomes
2. COVID-related case studies: how data was used, how rapid interventions were deployed, agile development methodolodies
Details
Erscheinungsjahr: 2020
Genre: Importe, Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Inhalt: xxx
407 S.
61 s/w Illustr.
407 p. 61 illus.
ISBN-13: 9781484265369
ISBN-10: 148426536X
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Panesar, Arjun
Auflage: 2nd ed.
Hersteller: Apress
Apress L.P.
Verantwortliche Person für die EU: Books on Demand GmbH, In de Tarpen 42, D-22848 Norderstedt, info@bod.de
Maße: 254 x 178 x 24 mm
Von/Mit: Arjun Panesar
Erscheinungsdatum: 16.12.2020
Gewicht: 0,822 kg
Artikel-ID: 118951081
Sicherheitshinweis