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- Articles and reports: 11-522-X202100100016Description: To build data capacity and address the U.S. opioid public health emergency, the National Center for Health Statistics received funding for two projects. The projects involve development of algorithms that use all available structured and unstructured data submitted for the 2016 National Hospital Care Survey (NHCS) to enhance identification of opioid-involvement and the presence of co-occurring disorders (coexistence of a substance use disorder and a mental health issue). A description of the algorithm development process is provided, and lessons learned from integrating data science methods like natural language processing to produce official statistics are presented. Efforts to make the algorithms and analytic datafiles accessible to researchers are also discussed.
Key Words: Opioids; Co-Occurring Disorders; Data Science; Natural Language Processing; Hospital Care
Release date: 2021-10-22
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- Articles and reports: 11-522-X202100100016Description: To build data capacity and address the U.S. opioid public health emergency, the National Center for Health Statistics received funding for two projects. The projects involve development of algorithms that use all available structured and unstructured data submitted for the 2016 National Hospital Care Survey (NHCS) to enhance identification of opioid-involvement and the presence of co-occurring disorders (coexistence of a substance use disorder and a mental health issue). A description of the algorithm development process is provided, and lessons learned from integrating data science methods like natural language processing to produce official statistics are presented. Efforts to make the algorithms and analytic datafiles accessible to researchers are also discussed.
Key Words: Opioids; Co-Occurring Disorders; Data Science; Natural Language Processing; Hospital Care
Release date: 2021-10-22
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