Medication Error Detection Using Contextual Language Models-猫眼课题宝

DOI：

发表时间：

2022-01

期刊：

ArXiv

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Yu Jiang;C. Poellabauer

用药错误最常发生在订购或开处方阶段，可能导致医疗并发症和不良的健康结果。虽然可以使用不同的技术来捕获这些错误；这项工作的重点是对处方信息进行文本和上下文分析，以检测和防止潜在的用药错误。在本文中，我们演示了如何使用基于 BERT 的上下文语言模型，基于从数千条患者记录的真实医疗数据中提取的数据集来检测书面或口头文本中的异常。所提出的模型能够学习文本依赖性模式并根据患者数据等上下文信息预测错误输出。实验结果表明，文本输入的准确率高达 96.63%，语音输入的准确率高达 79.55%，这对于大多数实际应用来说是令人满意的。

Medication errors most commonly occur at the ordering or prescribing stage, potentially leading to medical complications and poor health outcomes. While it is possible to catch these errors using different techniques; the focus of this work is on textual and contextual analysis of prescription information to detect and prevent potential medication errors. In this paper, we demonstrate how to use BERT-based contextual language models to detect anomalies in written or spoken text based on a data set extracted from real-world medical data of thousands of patient records. The proposed models are able to learn patterns of text dependency and predict erroneous output based on contextual information such as patient data. The experimental results yield accuracy up to 96.63% for text input and up to 79.55% for speech input, which is satisfactory for most real-world applications.

Medication Error Detection Using Contextual Language Models

文献摘要

相似文献