用多模型集成BERT提升电子病历中药物事件识别准确率
Ensemble BERT for Medication Event Classification on Electronic Health Records (EHRs)
- 构建基于不同预训练数据的BERT集成模型
- 在CMED数据集上实现微调后严格F1提升5%和6%
- 适合临床文本分析与医疗AI研究者参考
从电子健康记录（EHR）和临床笔记中识别药物、疾病及关系等关键变量，在临床领域具有广泛应用。n2c2 2022挑战赛构建了上下文药物事件数据集（CMED），本研究聚焦该挑战赛第一赛道第二子任务，即通过构建新型BERT集成模型，从临床笔记中检测并分类药物事件。研究首先在Wikipedia和MIMIC等大规模数据上预训练BERT模型，随后在CMED训练集上进行微调。多个微调后的BERT模型对测试集生成多组预测结果，采用投票策略融合为最终判断。实验表明，该方法使严格微平均F1得分提高约5%，严格宏平均F1得分提高约6%。
原文摘要 · Abstract (English)
Identification of key variables such as medications, diseases, relations from health records and clinical notes has a wide range of applications in the clinical domain. n2c2 2022 provided shared tasks on challenges in natural language processing for clinical data analytics on electronic health records (EHR), where it built a comprehensive annotated clinical data Contextualized Medication Event Dataset (CMED). This study focuses on subtask 2 in Track 1 of this challenge that is to detect and classify medication events from clinical notes through building a novel BERT-based ensemble model. It started with pretraining BERT models on different types of big data such as Wikipedia and MIMIC. Afterwards, these pretrained BERT models were fine-tuned on CMED training data. These fine-tuned BERT models were employed to accomplish medication event classification on CMED testing data with multiple predictions. These multiple predictions generated by these fine-tuned BERT models were integrated to build final prediction with voting strategies. Experimental results demonstrated that BERT-based ensemble models can effectively improve strict Micro-F score by about 5% and strict Macro-F score by about 6%, respectively.
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