ScAN: Suicide Attempt and Ideation Events Dataset

Bhanu Pratap Singh Rawat, Samuel Kovaly, Hong Yu, Wilfred Pigeon


Abstract
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.
Anthology ID:
2022.naacl-main.75
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1029–1040
Language:
URL:
https://aclanthology.org/2022.naacl-main.75
DOI:
10.18653/v1/2022.naacl-main.75
Bibkey:
Cite (ACL):
Bhanu Pratap Singh Rawat, Samuel Kovaly, Hong Yu, and Wilfred Pigeon. 2022. ScAN: Suicide Attempt and Ideation Events Dataset. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1029–1040, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
ScAN: Suicide Attempt and Ideation Events Dataset (Rawat et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.75.pdf
Code
 bsinghpratap/scan