Leveraging Contextual Relatedness to Identify Suicide Documentation in
Clinical Notes through Zero Shot Learning
- URL: http://arxiv.org/abs/2301.03531v1
- Date: Mon, 9 Jan 2023 17:26:07 GMT
- Title: Leveraging Contextual Relatedness to Identify Suicide Documentation in
Clinical Notes through Zero Shot Learning
- Authors: Terri Elizabeth Workman, Joseph L. Goulet, Cynthia A. Brandt, Allison
R. Warren, Jacob Eleazer, Melissa Skanderson, Luke Lindemann, John R.
Blosnich, John O Leary, Qing Zeng Treitler
- Abstract summary: This paper describes a novel methodology that identifies suicidality in clinical notes by addressing this data sparsity issue through zero-shot learning.
A deep neural network was trained by mapping the training documents contents to a semantic space.
In applying a 0.90 probability threshold, the methodology identified notes not associated with a relevant ICD 10 CM code that documented suicidality, with 94 percent accuracy.
- Score: 8.57098973963918
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Identifying suicidality including suicidal ideation, attempts, and risk
factors in electronic health record data in clinical notes is difficult. A
major difficulty is the lack of training samples given the small number of true
positive instances among the increasingly large number of patients being
screened. This paper describes a novel methodology that identifies suicidality
in clinical notes by addressing this data sparsity issue through zero-shot
learning. U.S. Veterans Affairs clinical notes served as data. The training
dataset label was determined using diagnostic codes of suicide attempt and
self-harm. A base string associated with the target label of suicidality was
used to provide auxiliary information by narrowing the positive training cases
to those containing the base string. A deep neural network was trained by
mapping the training documents contents to a semantic space. For comparison, we
trained another deep neural network using the identical training dataset labels
and bag-of-words features. The zero shot learning model outperformed the
baseline model in terms of AUC, sensitivity, specificity, and positive
predictive value at multiple probability thresholds. In applying a 0.90
probability threshold, the methodology identified notes not associated with a
relevant ICD 10 CM code that documented suicidality, with 94 percent accuracy.
This new method can effectively identify suicidality without requiring manual
annotation.
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