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Paper · 2205.07872 · NAACL · 2022

ScAN: Suicide Attempt and Ideation Events Dataset

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

arXiv · PDF · Open in the Atlas

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RepositoryRoleRan
medspacy/medspacy canonical 2 of 6
FunctionStatusWhere it lives
get_quickumls_demo_dir Ran medspacy/medspacy/medspacy/util.py
code served (permissive licence) · get_code("1a7539a620e770c5")
tuple_overlaps Ran medspacy/medspacy/medspacy/util.py
code served (permissive licence) · get_code("3c2bc7424bae3eca")
get_context_attributes Not yet run medspacy/medspacy/medspacy/_extensions.py
code served (permissive licence) · get_code("80758bcfdebc4a4b")
get_window_span Not yet run medspacy/medspacy/medspacy/_extensions.py
code served (permissive licence) · get_code("3f2b87b178dd7aa2")
get_window_token Not yet run medspacy/medspacy/medspacy/_extensions.py
code served (permissive licence) · get_code("7ccdc8e8d91c86f5")
prune_overlapping_matches Not yet run medspacy/medspacy/medspacy/common/util.py
code served (permissive licence) · get_code("dbb16d9652741b52")

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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 Retreiver), a multi-task RoBERTabased 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 1 .

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