rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Machine Learning and AI for Healthcare
    sub title: Big Data for Improved Health Outcomes
  name:
    name part: Panesar, Arjun.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: xxu
    date issued: 2021
    edition: 2nd ed. 2021.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (XXX, 407 pages 61 illustrations)
  abstract: 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. You will: 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.
  table of contents: Chapter 1: What Is Artificial Intelligence? -- Chapter 2: Data -- Chapter 3: What Is Machine Learning -- Chapter 4: Machine Learning Algorithms -- Chapter 5: How to Perform Machine Learning -- Chapter 6: Preparing Data -- Chapter 7: Evaluating Machine Learning Models -- Chapter 8: Machine Learning and AI Ethics -- Chapter 9: The Future of Healthcare -- Chapter 10: Case Studies -- Appendix A: References -- Appendix B: Glossary.
  note: by Arjun Panesar.
  subject:
    topic: Machine learning
  subject:
    topic: Computer software
  subject:
    topic: Machine Learning
  subject:
    topic: Professional Computing
  classification: Q325.5-.7
  classification: TK7882.P3
  classification: 006.31
  related item:
    title info:
      title: Springer Nature eBook
  identifier: 9781484265376
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record creation date: 201215
    record change date: 20230330185431.1
    record identifier: in00004421550