  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.
