Identifying suicide documentation in clinical notes through zero‐shot learning

Due to data sparsity, suicidality documentation is difficult to identify in clinical notes. Zero‐shot learning addresses the data sparsity issue. Zero‐shot learning enables identification of suicidality and its risks in clinical notes, and associated patients, where no diagnostic code relevant to su...

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Bibliographic Details
Published in:Health science reports 2023-09, Vol.6 (9), p.e1526-e1526
Main Authors: Workman, Terri Elizabeth, Goulet, Joseph L., Brandt, Cynthia A., Warren, Allison R., Eleazer, Jacob, Skanderson, Melissa, Lindemann, Luke, Blosnich, John R., O'Leary, John, Zeng‐Treitler, Qing
Format: Article
Language:English
Subjects:
Algorithms
Classification
computer
Datasets
Documentation
Electronic health records
international classification of diseases
Machine learning
Mental disorders
Mental health
natural language processing
Neural networks
Original Research
Patients
Prevention
Risk factors
Self destructive behavior
Semantics
Suicidal behavior
suicide
Suicides & suicide attempts
Veterans
Womens health
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Summary:Due to data sparsity, suicidality documentation is difficult to identify in clinical notes. Zero‐shot learning addresses the data sparsity issue. Zero‐shot learning enables identification of suicidality and its risks in clinical notes, and associated patients, where no diagnostic code relevant to suicide or self‐harm has been recorded.
ISSN:2398-8835
2398-8835
DOI:10.1002/hsr2.1526