In the project presented here, we used NLP tools for annotating German medical trainings documents with SNOMED CT codes. Following research question was addressed: Is it possible to automate the annotation of training documents with an NLP pipeline especially designed for this task but requiring translation into English? The goal of our stakeholder, an institution responsible for the continuing education of physicians, was to facilitate the switch between different medical trainings programs by coding the same requirement with the same SNOMED CT code, even if the wording is different. We first describe how we chose the concrete NLP tools, after which the concrete steps for implementing our prototype are outlined: the NLP pipeline construction, the implementation, and the validation. We infer three important lessons from our results: (i) self-supervision is no free lunch and should be based on a sophisticated task, (ii) the translation via DeepL can be too context-dependent for a peculiar use case, and (iii) ontology extraction can increase efficiency as well as accuracy.

To facilitate interaction with mobile health applications, chatbots are increasingly used. They realize the interaction as a dialog where users can ask questions and get answers from the chatbot. A big challenge is to create a comprehensive knowledge base comprising patterns and rules for representing possible user queries the chatbot has to understand and interpret. In this work, we assess how crowdsourcing can be used for generating examples of possible user queries for a medication chatbot. Within one week, the crowdworker generated 2‘738 user questions. The examples provide a large variety of possible formulations and information needs. As a next step, these examples for user queries will be used to train our medication chatbot.