Skip to main content

Research Repository

Advanced Search

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

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

Authors

N Kennedy

D C Brodbelt

D B Church

D G O'Neill



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 Nov 20, 2020
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

Files







You might also like



Downloadable Citations