http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Occupational injury surveillance methods using free text data and machine learning
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.
Liane Hirabayashi, Erika Scott, Paul Jenkins, Nicole Krupa.
Includes bibliographical references and index.
SAGE Research Methods Cases: Medicine and Health