  Suicide is an important public health concern and one of the leading causes
of death worldwide. Suicidal behaviors, including suicide attempts (SA) and
suicide ideations (SI), are leading risk factors for death by suicide.
Information related to patients' previous and current SA and SI are frequently
documented in the electronic health record (EHR) notes. Accurate detection of
such documentation may help improve surveillance and predictions of patients'
suicidal behaviors and alert medical professionals for suicide prevention
efforts. In this study, we first built Suicide Attempt and Ideation Events
(ScAN) dataset, a subset of the publicly available MIMIC III dataset spanning
over 12k+ EHR notes with 19k+ annotated SA and SI events information. The
annotations also contain attributes such as method of suicide attempt. We also
provide a strong baseline model ScANER (Suicide Attempt and Ideation Events
Retriever), a multi-task RoBERTa-based model with a retrieval module to extract
all the relevant suicidal behavioral evidences from EHR notes of an
hospital-stay and, and a prediction module to identify the type of suicidal
behavior (SA and SI) concluded during the patient's stay at the hospital.
ScANER achieved a macro-weighted F1-score of 0.83 for identifying suicidal
behavioral evidences and a macro F1-score of 0.78 and 0.60 for classification
of SA and SI for the patient's hospital-stay, respectively. ScAN and ScANER are
publicly available.
