Machine Learning and AI for Healthcare (eBook, PDF) - Panesar, Arjun
29,95 €
29,95 €
inkl. MwSt.
Sofort per Download lieferbar
29,95 €
inkl. MwSt.
Sofort per Download lieferbar

Alle Infos zum eBook verschenken
Als Download kaufen
29,95 €
inkl. MwSt.
Sofort per Download lieferbar
Abo Download
9,90 € / Monat*
*Abopreis beinhaltet vier eBooks, die aus der tolino select Titelauswahl im Abo geladen werden können.

inkl. MwSt.
Sofort per Download lieferbar

Einmalig pro Kunde einen Monat kostenlos testen (danach 9,90 € pro Monat), jeden Monat 4 aus 40 Titeln wählen, monatlich kündbar.

Mehr zum tolino select eBook-Abo
Jetzt verschenken
29,95 €
inkl. MwSt.
Sofort per Download lieferbar

Alle Infos zum eBook verschenken
15 °P sammeln

  • Format: PDF



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
…mehr

Produktbeschreibung


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.

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GB, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

  • Produktdetails
  • Verlag: Springer-Verlag GmbH
  • Erscheinungstermin: 04.02.2019
  • Englisch
  • ISBN-13: 9781484237991
  • Artikelnr.: 55116350
Autorenporträt
Arjun Panesar is the founder of Diabetes Digital Media (DDM), the world's largest diabetes community and provider of evidence-based digital health interventions. Arjun holds a first-class honors degree (MEng) in Computing and Artificial Intelligence from Imperial College, London. Benefiting from a decade of experience in big data and affecting user outcomes, Arjun 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.

Inhaltsangabe
Chapter 1: What is Artificial Intelligence 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: Data Chapter Goal: To understand data required for learning and how to ensure valid data for outcome veracity No of pages: 30 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. The key aspects required of data, in particular, validity to ensure that only useful and relevant information 3. 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 data 5. Challenges faced as part of the use of big data 6. 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, applications and their mathematical basis 5. Software(s) used for learning 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 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: 10 1. How to evaluate machine learning systems 2. Methodologies for evaluating outputs 3. Improving your intelligence 4. Advanced analytics 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? 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. What about the ethics? 7. Conclusion Chapter 8: Case studies Chapter Goal: Real world applications of AI and machine/deep learning in healthcare No of pages: 20 1. Real world case studies of organizations implementing machine learning and the challenges, methodologies, algorithms and analytics used to determine optimal performance/outcomes