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 for healthcare applications
  name:
    name part: Mohanty, Sachi Nandan
    role:
      role term: editor.
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: nju
    date issued: 2021
    copyright date: 2021
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xx, 389 pages) : illustrations
  abstract: "When considering the idea of using machine learning in healthcare, it is a Herculean task to present the entire gamut of information in the field of intelligent systems. It is, therefore the objective of this book to keep the presentation narrow and intensive. This approach is distinct from others in that it presents detailed computer simulations for all models presented with explanations of the program code. It includes unique and distinctive chapters on disease diagnosis, telemedicine, medical imaging, smart health monitoring, social media healthcare, and machine learning for COVID-19. These chapters help develop a clear understanding of the working of an algorithm while strengthening logical thinking. In this environment, answering a single question may require accessing several data sources and calling on sophisticated analysis tools. While data integration is a dynamic research area in the database community, the specific needs of research have led to the development of numerous middleware systems that provide seamless data access in a result-driven environment. Since this book is intended to be useful to a wide audience, students, researchers and scientists from both academia and industry may all benefit from this material. It contains a comprehensive description of issues for healthcare data management and an overview of existing systems, making it appropriate for introductory and instructional purposes. Prerequisites are minimal; the readers are expected to have basic knowledge of machine learning. This book is divided into 22 real-time innovative chapters which provide a variety of application examples in different domains. These chapters illustrate why traditional approaches often fail to meet customers' needs. The presented approaches provide a comprehensive overview of current technology. Each of these chapters, which are written by the main inventors of the presented systems, specifies requirements and provides a description of both the chosen approach…
  note: edited by Sachi Nandan Mohanty [and three others].
  note: Includes bibliographical references and index.
  subject:
    topic: Medical informatics
  subject:
    topic: Machine learning
  subject:
    topic: Medicine
    topic: Data processing
  subject:
    topic: Medical Informatics
  subject:
    topic: Machine learning
  classification: R858 .M334 2021
  classification: 610.285
  classification: W 26.5
  related item:
    title info:
      title: Machine learning for healthcare applications
    origin info:
      publisher: Hoboken, NJ : Wiley-Scrivener, 2021.
    identifier: (DLC) 2021014072
  identifier: 9781119792598
  identifier: 1119792592
  identifier: 9781119792604
  identifier: 1119792606
  identifier: 9781119792611
  identifier: 1119792614
  identifier: 9781119791812
  identifier: 1119791812
  identifier: 2021014073
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: DLC
    record creation date: 210324
    record change date: 20260214200953.3
    record identifier: in00004687913
    language of cataloging:
      language term: eng