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

将 ICD 代码转换为缩写伤害量表的新颖深度学习策略

• 批准号: 10532796
• 负责人: Thomas Ryan Hartka
• 金额: $ 8.08万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-12-01 至 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10532796
• 关键词: Abbreviations; Accident and Emergency department; Address; Algorithms; American College of Surgeons; Anatomy; Biomechanics; Body Regions; Cause of Death; Certification; Cessation of life; Child; Classification; Code; Comparative Effectiveness Research; Computational Linguistics; Data; Data Science; Databases; Diagnosis; Disease; Disparity; Effectiveness of Interventions; Elements; Emergency Medicine; European Union; Goals; Grant; Individual; Injury; Injury Severity Score; International; International Classification of Disease Codes; International Classification of Diseases; Intervention; K-Series Research Career Programs; Language; Language Development; Learning Module; Life; Location; Machine Learning; Maps; Measurement; Measures; Medical; Methods; Modeling; Modernization; Monitor; National Center for Advancing Translational Sciences; Natural Language Processing; Outcome; Patients; Pattern; Policies; Population; Prevention Research; Research; Risk Factors; Safety; Science; Severities; Specific qualifier value; System; Techniques; Training; Translating; Translations; Trauma; Trauma Research; Trauma patient; Traumatic injury; Vehicle crash; Work; World Health Organization; administrative database; care delivery; comparative effectiveness; data integration; data interoperability; data repository; deep learning; deep learning model; disability; disease diagnosis; improved; individual patient; injury prevention; injury surveillance; innovation; learning strategy; mortality; novel; novel strategies; predictive modeling; programs; recurrent neural network; relative effectiveness; severe injury; success; tool; trauma care; vector

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.

项目摘要 创伤是美国和世界各地死亡和残疾的主要原因之一。准确 测量是至关重要的，以提高我们对这种疾病的理解和衡量的有效性， 干预措施。跟踪创伤性伤害的负担不仅依赖于确定死亡，还依赖于非致命性伤害。 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。没有直接测量损伤严重程度的机制。为了 在严重程度方面，ICD代码通常被转换为简明损伤量表（AIS）。每个AIS代码都有一个 相对损伤严重度的测量，并且可以组合多个代码来确定总体损伤严重度 个别患者的。然而，目前使用的ICD转换为AIS的方法依赖于一对一的转换。 这些编码系统之间的映射具有许多固有的困难。具体来说，这些一对一 映射已经显示出系统地低估了整体损伤的严重性。的最新进展 计算语言学通过使用嵌入和深度学习解决了非常相似的问题。我们 我打算把ICD的这些技术应用到AIS翻译中。关键的创新是考虑所有的信息 可以同时获得关于患者的信息，而不是孤立地转换每个代码。这一目标 R03提案旨在开发工具，以提高使用ICD的人群水平伤害研究的准确性 代码.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体的整体损伤严重程度 患者从ICD代码，和（2）开发一个工具，将ICD代码转换为AIS的个别患者。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， 具有相似含义的单词彼此接近，并且相对位置编码单词之间的关系。 同样，我们将把ICD转移到一个嵌入式空间中，这个空间将被后续的深度学习模块使用 产生我们的结果。国家创伤数据库收集了数百万创伤患者的数据， （NTDB），其包含由专家编码器提取的ICD和AIS。我们将使用这些数据来训练和评估 深度学习模型将成为我们工具的基础。总之，这些工具将满足关键的需求， 创伤研究的质量和使用行政医疗提高伤害监测的准确性 数据库。