# ScAN: Suicide Attempt and Ideation Events Dataset

arXiv: https://arxiv.org/abs/2205.07872 · HTML: https://mlsanity.com/paper/2205.07872/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 12 May 2022.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications](https://mlsanity.com/paper/1910.12611/) (2019)
- [Predicting Adolescent Suicide Attempts with Neural Networks](https://mlsanity.com/paper/1711.10057/) (2017)

## Built on by (most cited first, 8 of 8)

- [Can Large Language Models Identify Implicit Suicidal Ideation? An Empirical Evaluation](https://mlsanity.com/paper/2502.17899/) (2025)
- [PsyGUARD: An Automated System for Suicide Detection and Risk Assessment in Psychological Counseling](https://mlsanity.com/paper/2409.20243/) (2024)
- [Detecting Early and Implicit Suicidal Ideation via Longitudinal and Information Environment Signals on Social Media](https://mlsanity.com/paper/2510.14889/) (2025)
- [Suicide Phenotyping from Clinical Notes in Safety-Net Psychiatric Hospital Using Multi-Label Classification with Pre-Trained Language Models](https://mlsanity.com/paper/2409.18878/) (2024)
- [Harmful Suicide Content Detection](https://mlsanity.com/paper/2407.13942/) (2024)
- [Multi-Label Classification with Generative AI Models in Healthcare: A Case Study of Suicidality and Risk Factors](https://mlsanity.com/paper/2507.17009/) (2025)
- [Reliable Self-Harm Risk Screening via Adaptive Multi-Agent LLM Systems](https://mlsanity.com/paper/2604.22154/) (2026)
- [Before the Labels: How Dataset Construction Shapes Suicidality Detection in Clinical Text](https://mlsanity.com/paper/2606.19637/) (2026)

## Abstract (arXiv)

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.