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Occupational injury surveillance methods using free text data and machine learning : creating a gold standard data set / Liane Hirabayashi, Erika Scott, Paul Jenkins, Nicole Krupa.
- Format:
- Book
- Author/Creator:
- Hirabayashi, Liane, author.
- Scott, Erika, author.
- Jenkins, Paul, active 2020, author.
- Krupa, Nicole, author.
- Series:
- SAGE Research Methods Cases: Medicine and Health.
- SAGE Research Methods Cases: Medicine and Health
- Language:
- English
- Subjects (All):
- Industrial accidents--Data processing--Case studies.
- Industrial accidents.
- Machine learning--Case studies.
- Machine learning.
- Physical Description:
- 1 online resource.
- Place of Publication:
- London : SAGE Publications Ltd, 2020.
- Summary:
- 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.
- Notes:
- Includes bibliographical references and index.
- Description based on XML content.
- ISBN:
- 1-5297-2048-6
- 9781529720488
- OCLC:
- 1142449027
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