  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.
