用预训练模型从病历中识别自杀风险，提升精神科诊疗效率
Suicide Phenotyping from Clinical Notes in Safety-Net Psychiatric Hospital Using Multi-Label Classification with Pre-Trained Language Models
- 采用多标签分类策略，基于BERT改进模型识别四种自杀相关事件
- RoBERTa模型在单任务与多任务微调下分别达86%准确率和88%准确率
- 适合临床决策支持系统开发，尤其关注精神卫生数据挖掘的团队
准确识别和分类自杀事件可改善高危精神科环境中的预防措施，减轻工作负担并提升照护质量。预训练语言模型有望从非结构化临床记录中识别自伤倾向。本研究评估了四种基于BERT的模型在两种微调策略（多个单标签与单一多标签）下的表现，用于检测500份标注的病历笔记中的自杀意念（SI）、自杀未遂（SA）、接触自杀（ES）及非自杀性自伤（NSSI）。结果表明，采用多单标签分类策略时，RoBERTa表现最优（准确率=0.86，F1=0.78）；MentalBERT（准确率=0.83，F1=0.74）优于BioClinicalBERT（准确率=0.82，F1=0.72），后者又优于BERT（准确率=0.80，F1=0.70）。而使用单一多标签分类策略微调的RoBERTa进一步提升性能（准确率=0.88，F1=0.81）。研究强调，模型优化、领域相关预训练数据以及单个多标签分类策略显著提升了自杀表型识别效果。
原文摘要 · Abstract (English)
Accurate identification and categorization of suicidal events can yield better suicide precautions, reducing operational burden, and improving care quality in high-acuity psychiatric settings. Pre-trained language models offer promise for identifying suicidality from unstructured clinical narratives. We evaluated the performance of four BERT-based models using two fine-tuning strategies (multiple single-label and single multi-label) for detecting coexisting suicidal events from 500 annotated psychiatric evaluation notes. The notes were labeled for suicidal ideation (SI), suicide attempts (SA), exposure to suicide (ES), and non-suicidal self-injury (NSSI). RoBERTa outperformed other models using multiple single-label classification strategy (acc=0.86, F1=0.78). MentalBERT (acc=0.83, F1=0.74) also exceeded BioClinicalBERT (acc=0.82, F1=0.72) which outperformed BERT (acc=0.80, F1=0.70). RoBERTa fine-tuned with single multi-label classification further improved the model performance (acc=0.88, F1=0.81). The findings highlight that the model optimization, pretraining with domain-relevant data, and the single multi-label classification strategy enhance the model performance of suicide phenotyping. Keywords: EHR-based Phenotyping; Natural Language Processing; Secondary Use of EHR Data; Suicide Classification; BERT-based Model; Psychiatry; Mental Health
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