# From Witch's Shot to Music Making Bones -- Resources for Medical Laymen to Technical Language and Vice Versa

arXiv: https://arxiv.org/abs/2005.11494 · HTML: https://mlsanity.com/paper/2005.11494/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2020.

## Built on by (most cited first, 6 of 6)

- [Claim Detection in Biomedical Twitter Posts](https://mlsanity.com/paper/2104.11639/) (2021)
- [Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR)](https://mlsanity.com/paper/2204.09952/) (2022)
- [A Medical Information Extraction Workbench to Process German Clinical Text](https://mlsanity.com/paper/2207.03885/) (2022)
- [Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective](https://mlsanity.com/paper/2208.02031/) (2022)
- [GERNERMED -- An Open German Medical NER Model](https://mlsanity.com/paper/2109.12104/) (2021)
- [Am I eligible? Natural Language Inference for Clinical Trial Patient Recruitment: the Patient's Point of View](https://mlsanity.com/paper/2503.15718/) (2025)

## Abstract (arXiv)

Many people share information in social media or forums, like food they eat, sports activities they do or events which have been visited. This also applies to information about a person's health status. Information we share online unveils directly or indirectly information about our lifestyle and health situation and thus provides a valuable data resource. If we can make advantage of that data, applications can be created that enable e.g. the detection of possible risk factors of diseases or adverse drug reactions of medications. However, as most people are not medical experts, language used might be more descriptive rather than the precise medical expression as medics do. To detect and use those relevant information, laymen language has to be translated and/or linked to the corresponding medical concept. This work presents baseline data sources in order to address this challenge for German. We introduce a new data set which annotates medical laymen and technical expressions in a patient forum, along with a set of medical synonyms and definitions, and present first baseline results on the data.