Novel deep learning strategy to translate ICD Codes to the Abbreviated Injury Scale

项目总结 创伤是美国和世界各地导致死亡和残疾的主要原因之一。准确 测量对于提高我们对这种疾病的了解和测量治疗的有效性至关重要 干预措施。追踪创伤性损伤的负担不仅取决于确定死亡人数，还取决于确定非致命性死亡人数 受伤。广泛使用的国际疾病分类(ICD)诊断编码系统，由 世界卫生组织。没有直接测量伤害严重程度的机制。为了 在衡量严重程度时，ICD代码通常被转换为缩写伤害等级(AIS)。每个AIS代码都有一个 相对伤害严重程度的测量，可以组合多个代码来确定总体伤害严重程度 单个病人的身份。然而，目前使用的ICD到AIS的转换方法依赖于一对一 这些编码系统之间的映射，这具有许多固有的困难。具体来说，这些一对一 映射已被证明系统性地低估了总体伤害严重程度。的最新进展 计算语言学已经使用嵌入和深度学习解决了非常类似的问题。我们 打算将这些技术应用到AIS翻译中。关键的创新是考虑到所有的信息 同时提供有关患者的信息，而不是孤立地转换每个代码。这个目标就是 R03提案是开发工具，以提高使用ICD的人群水平伤害研究的准确性 密码。我们将通过以下方式实现这一目标：(1)开发一种工具来预测个人的整体伤害严重程度 (2)开发了一个工具，用于将ICD代码转换为针对个别患者的AIS。现代 语言翻译的算法是基于确定单词在嵌入空间中的位置，因此 意思相近的词彼此接近，相对位置编码了词之间的关系。 类似地，我们将ICD转移到嵌入式空间，供后续深度学习模块使用 产生我们的结果。在国家创伤数据库中收集了数百万创伤患者的数据 (NTDB)，包含由专家编码员提取的ICD和AIS。我们将使用这些数据进行训练和评估 深度学习模型将成为我们工具的基础。综合起来，这些工具将满足改进的关键需求 提高创伤研究质量，利用行政医疗提高损伤监测的准确性 数据库。

PROJECT SUMMARY Trauma is one of the leading causes of death and disability in the US and around the world. Accurate measurement is critical to improving our understanding of this disease and gauging the effectiveness of interventions. Tracking the burden of traumatic injuries relies on not only identifying deaths, but also non-fatal injuries. The widely used International Classification of Disease (ICD) diagnosis coding system, developed by the World Health Organization. does not have a mechanism for directly measuring injury severity. In order to measure in severity, ICD codes are often converted to the Abbreviated Injury Scale (AIS). Each AIS code has a measure of relative injury severity, and multiple codes can be combined to determine the overall injury severity of an individual patients. However, the currently used methods for conversion of ICD to AIS rely on one-to-one mapping between these coding systems, which has many inherent difficulties. Specifically, these one-to-one mappings have been shown to systematically underestimate overall injury severity. Recent advances in computation linguistics have solved very similar problems with the use of embedding and deep learning. We intended to apply these techniques ICD to AIS translations. The key innovation is to consider all the information available about a patient simultaneously, rather than converting each code in isolation. This objective of this R03 proposal is to develop tools that improve the accuracy of population-level injury research that uses ICD codes. We will accomplish this objective by: (1) developing a tool to predict overall injury severity for individual patients from ICD codes, and (2) developing a tool to translate ICD codes to AIS for individual patients. Modern language translation has algorithms are based on determining the location of words in an embedded space, so words with similar meaning are near to each other and the relative locations encode relationships between words. Similarly, we will transfer ICD into an embedded space, which will be used by subsequent deep learning modules produce our results. There is data for millions of trauma patients collected in in the National Trauma Data Bank (NTDB) that contains both ICD and AIS extracted by expert coders. We will use this data to train and evaluate the deep learning models that will underlie our tools. Together, these tools will meet the critical needs to improve the quality of trauma research and increase the accuracy of injury monitoring using administrative medical databases.