Detecting false-positive disease references in veterinary clinical notes without manual annotations

Kennedy, N; Brodbelt, D C; Church, D B; O'Neill, D G

Abstract

Clinicians often include references to diseases in clinical notes, which have not been diagnosed in their patients. For some diseases terms, the majority of disease references written in the patient notes may not refer to true disease diagnosis. These references occur because clinicians often use their clinical notes to speculate about disease existence (differential diagnosis) or to state that the disease has been ruled out. To train classifiers for disambiguating disease references, previous researchers built training sets by manually annotating sentences. We show how to create very large training sets without the need for manual annotation. We obtain state-of- the-art classification performance with a bidirectional long short-term memory model trained to distinguish disease references between patients with or without the disease diagnosis in veterinary clinical notes.

Citation

Kennedy, N., Brodbelt, D. C., Church, D. B., & O'Neill, D. G. (2019). Detecting false-positive disease references in veterinary clinical notes without manual annotations. https://doi.org/10.1038/s41746-019-0108-y

• Journal Article Type: Article
• Acceptance Date: Apr 12, 2019
• Publication Date: May 3, 2019
• Deposit Date: May 29, 2019
• Publicly Available Date: May 31, 2019
• Journal: npj Digital Medicine
• Peer Reviewed: Peer Reviewed
• Volume: 2
• Pages: 33
• DOI: https://doi.org/10.1038/s41746-019-0108-y
• Public URL: https://rvc-repository.worktribe.com/output/1382505