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: Occupational injury surveillance methods using free text data and machine learning
    sub title: creating a gold standard data set
  name:
    name part: Hirabayashi, Liane
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Scott, Erika
    role:
      role term: author.
  name:
    name part: Jenkins, Paul
    name part: active 2020
    role:
      role term: author.
  name:
    name part: Krupa, Nicole
    role:
      role term: author.
  type of resource: text
  genre: bibliography
  genre: Case studies.
  origin info:
    place:
      place term: enk
    date issued: 2020
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: Non-fatal injuries in agriculture, forestry, and fishing industries are under-reported in national surveillance data. To address this knowledge gap, our research team is developing an injury surveillance system that mines two electronic data sources: ambulance pre-hospital care reports and hospital discharge records. These methods allow researchers to identify occupationally related injury events without the use of industry or occupation codes in the data. In this case study, we will describe our methods to enhance the review of free-text data in pre-hospital care report records. In particular, we will review the steps we took to create a gold standard data set that will serve as the validation and training data set for machine learning.
  target audience: specialized
  note: Liane Hirabayashi, Erika Scott, Paul Jenkins, Nicole Krupa.
  note: Includes bibliographical references and index.
  subject:
    topic: Industrial accidents
    topic: Data processing
    genre: Case studies
  subject:
    topic: Machine learning
    genre: Case studies
  subject:
    topic: Apprentissage automatique
    genre: Études de cas
  subject:
    topic: Industrial accidents
    topic: Data processing
  subject:
    topic: Machine learning
  classification: HD7262 .H57 2020
  classification: 658.382
  related item:
    title info:
      title: SAGE Research Methods Cases: Medicine and Health
  identifier: 9781529720488
  identifier: 1529720486
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: SGPBL
    record creation date: 200123
    record change date: 20240923170308.8
    record identifier: in00005494265
    language of cataloging:
      language term: eng