论文标题
AKI-BERT：一种预测急性肾脏损伤的预训练的临床语言模型
AKI-BERT: a Pre-trained Clinical Language Model for Early Prediction of Acute Kidney Injury
论文作者
论文摘要
急性肾脏损伤（AKI）是一种常见的临床综合征，其特征是在几个小时或几天内突然发作肾衰竭或肾脏损伤。对ICU患者的AKI的准确预测比其他人更有可能具有AKI的患者可以及时进行干预，并减少AKI的并发症。与AKI相关的许多临床信息都是在很大程度上是非结构化文本的临床注释中捕获的，需要先进的自然语言处理（NLP）才能提取有用的信息。另一方面，最近培训的上下文语言模型，例如来自变形金刚（BERT）的双向编码器表示，最近在一般域中的许多NLP任务都改进了性能。但是，很少有人探讨了疾病特异性医学领域任务（例如AKI早期预测）的BERT。在本文中，我们试图将BERT应用于特定疾病，并提出基于BERT（AKI-BERT）的AKI领域特定的预训练的语言模型，该模型可用于挖掘临床注释以早期预测AKI。 Aki-Bert是在患有AKI风险的患者临床注释上预先训练的BERT模型。我们对重症监护III（模拟III）数据集的医学信息MART的实验表明，Aki-Bert可以为早期AKI预测产生性能改善，从而将BERT模型的实用性从一般临床领域扩展到疾病特异性领域。
Acute kidney injury (AKI) is a common clinical syndrome characterized by a sudden episode of kidney failure or kidney damage within a few hours or a few days. Accurate early prediction of AKI for patients in ICU who are more likely than others to have AKI can enable timely interventions, and reduce the complications of AKI. Much of the clinical information relevant to AKI is captured in clinical notes that are largely unstructured text and requires advanced natural language processing (NLP) for useful information extraction. On the other hand, pre-trained contextual language models such as Bidirectional Encoder Representations from Transformers (BERT) have improved performances for many NLP tasks in general domain recently. However, few have explored BERT on disease-specific medical domain tasks such as AKI early prediction. In this paper, we try to apply BERT to specific diseases and present an AKI domain-specific pre-trained language model based on BERT (AKI-BERT) that could be used to mine the clinical notes for early prediction of AKI. AKI-BERT is a BERT model pre-trained on the clinical notes of patients having risks for AKI. Our experiments on Medical Information Mart for Intensive Care III (MIMIC-III) dataset demonstrate that AKI-BERT can yield performance improvements for early AKI prediction, thus expanding the utility of the BERT model from general clinical domain to disease-specific domain.
