Medical coding (MC) is an essential pre-requisite for reliable data retrieval and reporting. Given a free-text reported term (RT) such as "pain of right thigh to the knee", the task is to identify the matching lowest-level term (LLT) - in this case "unilateral leg pain" - from a very large and continuously growing repository of standardized medical terms. However, automating this task is challenging due to a large number of LLT codes (as of writing over 80,000), limited availability of training data for long tail/emerging classes, and the general high accuracy demands of the medical domain. With this paper, we introduce the MC task, discuss its challenges, and present a novel approach called xTARS that combines traditional BERT-based classification with a recent zero/few-shot learning approach (TARS). We present extensive experiments that show that our combined approach outperforms strong baselines, especially in the few-shot regime. The approach is developed and deployed at Bayer, live since November 2021. As we believe our approach potentially promising beyond MC, and to ensure reproducibility, we release the code to the research community.

本研究介绍了医学编码任务及其挑战，提出了一种结合BERT分类和TARS最近的零/少样本学习方法的新方法xTARS，通过大量实验证明了该方法在此任务中的优越性，特别是在少样本学习方面，该方法已于2021年11月在拜耳实现，代码向研究社区公开以确保可复现性。

使用生物医学转换器集合和零/少样本学习进行医疗编码