论文标题
医疗法规从临床注释：从人类编码器到机器
Medical Codes Prediction from Clinical Notes: From Human Coders to Machines
论文作者
论文摘要
从临床笔记中对医疗法规的预测是当前医疗系统中每个医疗保健提供组织的实用且必需的需求。自动注释将节省人类编码人员今天花费的大量时间和过多的精力。但是，最大的挑战是直接从非结构化的自由文本临床注释中直接从数千种高维代码中确定适当的医疗法规。这项复杂的医疗法规预测问题从临床注释中引起了NLP社区的重大兴趣，最近的一些研究表明，全面的基于深度学习的方法的最新代码预测结果。这一进展提出了一个基本问题，即自动化机器学习系统与人类编码人员的工作绩效有多远，以及当前解释性方法如何适用于高级神经网络模型（例如变形金刚）的重要问题。这是为了预测支持代码预测的临床注释中的正确代码和目前的参考，因为预测结果的解释性和准确性对于从专业医疗编码者那里获得信任至关重要。
Prediction of medical codes from clinical notes is a practical and essential need for every healthcare delivery organization within current medical systems. Automating annotation will save significant time and excessive effort that human coders spend today. However, the biggest challenge is directly identifying appropriate medical codes from several thousands of high-dimensional codes from unstructured free-text clinical notes. This complex medical codes prediction problem from clinical notes has received substantial interest in the NLP community, and several recent studies have shown the state-of-the-art code prediction results of full-fledged deep learning-based methods. This progress raises the fundamental question of how far automated machine learning systems are from human coders' working performance, as well as the important question of how well current explainability methods apply to advanced neural network models such as transformers. This is to predict correct codes and present references in clinical notes that support code prediction, as this level of explainability and accuracy of the prediction outcomes is critical to gaining trust from professional medical coders.
