ScAN: Suicide Attempt and Ideation Events Dataset.

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 uicide ttempt and Ideatio Events (ScAN) dataset, a subset of the publicly available MIMIC III dataset spanning over 12+ EHR notes with 19+ 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 (uiide ttempt and Ideatio vents etreiver), a multi-task RoBERTa-based model with a to extract all the relevant suicidal behavioral evidences from EHR notes of an hospital-stay and, and a 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.