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arXiv 2409.18878cs.CLcs.AIcs.CYcs.IR

使用预训练语言模型的多标签分类在安全网精神病医院临床记录中进行自杀表型识别

Suicide Phenotyping from Clinical Notes in Safety-Net Psychiatric Hospital Using Multi-Label Classification with Pre-Trained Language Models

The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）；McGovern Medical School（麦戈文医学院）；Yale University（耶鲁大学）

Zehan Li, Yan Hu, Scott Lane, Salih Selek, Lokesh Shahani, Rodrigo Machado-Vieira, Jair Soares, Hua Xu, Hongfang Liu, Ming Huang

备注：
submitted to AMIA Informatics Summit 2025 as a conference paper
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AI总结：

本文比较了四种BERT类预训练语言模型在精神病临床记录中识别自杀意念、自杀未遂、自杀暴露和非自杀性自伤等多标签共存事件的表现，发现RoBERTa结合单多标签分类策略效果最佳。

摘要：

对自杀事件进行准确识别和分类可以带来更好的自杀预防措施，减少操作负担，并提高高危急症精神病环境中的护理质量。预训练语言模型为从非结构化临床叙述中识别自杀倾向提供了前景。我们评估了四种基于BERT的模型在两种微调策略（多个单标签和单个多标签）下检测500份标注精神病评估记录中共存自杀事件的表现。这些记录被标注为自杀意念（SI）、自杀未遂（SA）、自杀暴露（ES）和非自杀性自伤（NSSI）。RoBERTa在使用多个单标签分类策略时优于其他模型（acc=0.86，F1=0.78）。MentalBERT（acc=0.83，F1=0.74）也超过了BioClinicalBERT（acc=0.82，F1=0.72），而后者优于BERT（acc=0.80，F1=0.70）。使用单个多标签分类微调的RoBERTa进一步提高了模型性能（acc=0.88，F1=0.81）。研究结果强调，模型优化、使用领域相关数据进行预训练以及单个多标签分类策略能够提升自杀表型识别的模型性能。

英文摘要：

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

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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