Comparing algorithms for deriving psychosis diagnoses from longitudinal administrative clinical records
Journal article
Sara, Grant, Luo, Luming, Carr, Vaughn J., Raudino, Alessandra, Green, Melissa J., Laurens, Kristin R., Dean, Kimberlie, Cohen, Martin, Burgess, Philip and Morgan, Vera A.. (2014). Comparing algorithms for deriving psychosis diagnoses from longitudinal administrative clinical records. Social Psychiatry and Psychiatric Epidemiology. 49(11), pp. 1729 - 1737. https://doi.org/10.1007/s00127-014-0881-5
Authors | Sara, Grant, Luo, Luming, Carr, Vaughn J., Raudino, Alessandra, Green, Melissa J., Laurens, Kristin R., Dean, Kimberlie, Cohen, Martin, Burgess, Philip and Morgan, Vera A. |
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Abstract | Registers derived from administrative datasets are valuable tools in psychosis research, but diagnostic accuracy can be problematic. We sought to compare the relative performance of four methods for assigning a single diagnosis from longitudinal administrative clinical records when compared with reference diagnoses. |
Keywords | psychosis; diagnosis; algorithm; registers |
Year | 2014 |
Journal | Social Psychiatry and Psychiatric Epidemiology |
Journal citation | 49 (11), pp. 1729 - 1737 |
Publisher | Springer Medizin |
ISSN | 0933-7954 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s00127-014-0881-5 |
Scopus EID | 2-s2.0-84919866383 |
Page range | 1729 - 1737 |
Publisher's version | File Access Level Controlled |
Place of publication | Germany |
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https://acuresearchbank.acu.edu.au/item/8qq79/comparing-algorithms-for-deriving-psychosis-diagnoses-from-longitudinal-administrative-clinical-records
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