Electronic Medical Records contain clinical narrative text that is of great potential value to medical researchers. However, this information is mixed with Personally Identifiable Information that presents risks to patient and clinician confidentiality. This paper presents an end-to-end de-identification framework to automatically remove PII from hospital discharge summaries. Our corpus included 600 hospital discharge summaries which were extracted from the EMRs of two principal referral hospitals in Sydney, Australia. Our end-to-end de-identification framework consists of three components: 1) Annotation: labelling of PII in the hospital discharge summaries using five pre-defined categories: person, address, date of birth, individual identification number, phone/fax number; 2) Modelling: training six named entity recognition deep learning base-models on balanced and imbalanced datasets; and evaluating ensembles that combine all six base-models, the three base-models with the best F1 scores and the three base-models with the best recall scores respectively, using token-level majority voting and stacking methods; and 3) De-identification: removing PII from the hospital discharge summaries. Our results showed that the ensemble model combined using the stacking Support Vector Machine method on the three base-models with the best F1 scores achieved excellent results with a F1 score of 99.16% on the test set of our corpus. We also evaluated the robustness of our modelling component on the 2014 i2b2 de-identification dataset. Our ensemble model, which uses the token-level majority voting method on all six base-models, achieved the highest F1 score of 96.24% at strict entity matching and the highest F1 score of 98.64% at binary token-level matching compared to two state-of-the-art methods. The end-to-end framework provides a robust solution to de-identifying clinical narrative corpuses safely.

翻译：医疗记录中包含对医学研究人员具有巨大潜在价值的临床叙述文本。然而,这一信息与对病人和临床保密构成风险的个人识别信息混杂在一起。本文件展示了一个端至端去身份识别框架,以自动将PII从医院出院摘要中删除。我们的文件包括600份医院出院摘要,摘自澳大利亚悉尼两家主要转诊医院的EMMR。我们的端至端去身份认定框架包括三个组成部分:(1) 说明:在医院出院摘要中贴上PII标签,使用五个预定义类别:个人、地址、出生日期、个人身份号码、电话/传真号码;(2) 模型:在平衡和不平衡的数据集上培训6个命名实体确认深学习基础模型;以及评估将所有6个基模、3个基底模型与最好的F1级最佳F1分数和3个基底模型,使用象征性多数多数投票模式,在2014年出院出院后,我们完成了PII的交付情况摘要摘要摘要摘要。我们的成果显示,在F1级最高选举等级的模型上,在F1级最高等级测试标准中,我们完成了一个最高级的排名数级的模型,在F1级上,在F1级上,在F级上,在F级一级实现了一个最高级的排名数级上,在比数级的排名数级上,在比数级的排名数级测试结果。