论文标题
使用上下文语言模型检测药物错误检测
Medication Error Detection Using Contextual Language Models
论文作者
论文摘要
药物错误最常见于订购或处方阶段，可能导致医疗并发症和健康状况不佳。虽然可以使用不同的技术捕获这些错误；这项工作的重点是对处方信息的文本和上下文分析，以检测和防止潜在的药物错误。在本文中，我们演示了如何使用基于BERT的上下文语言模型来检测基于从数千个患者记录的实际医学数据中提取的数据集中的书面或口语文本中的异常情况。所提出的模型能够学习文本依赖性的模式，并根据上下文信息（例如患者数据）预测错误的输出。实验结果的文本输入的准确度高达96.63％，语音输入最高为79.55％，这对于大多数真实世界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.
