# Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR)

arXiv: https://arxiv.org/abs/2204.09952 · HTML: https://mlsanity.com/paper/2204.09952/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 53 references that are in the map.
- First posted on arXiv 21 Apr 2022.

## Builds on (most cited first, 9 of 9)

- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic](https://mlsanity.com/paper/2106.03794/) (2021)
- [COMETA: A Corpus for Medical Entity Linking in the Social Media](https://mlsanity.com/paper/2010.03295/) (2020)
- [Did You Really Just Have a Heart Attack? Towards Robust Detection of Personal Health Mentions in Social Media](https://mlsanity.com/paper/1802.09130/) (2018)
- [Weibo-COV: A Large-Scale COVID-19 Social Media Dataset from Weibo](https://mlsanity.com/paper/2005.09174/) (2020)
- [Relation extraction from clinical texts using domain invariant convolutional neural network](https://mlsanity.com/paper/1606.09370/) (2016)
- [From Witch's Shot to Music Making Bones -- Resources for Medical Laymen to Technical Language and Vice Versa](https://mlsanity.com/paper/2005.11494/) (2020)
- [Using Neural Networks for Relation Extraction from Biomedical Literature](https://mlsanity.com/paper/1905.11391/) (2019)
- [Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning](https://mlsanity.com/paper/2110.15703/) (2021)

## Built on by (most cited first, 4 of 4)

- [AIONER: All-in-one scheme-based biomedical named entity recognition using deep learning](https://mlsanity.com/paper/2211.16944/) (2022)
- [What Makes Medical Claims (Un)Verifiable? Analyzing Entity and Relation Properties for Fact Verification](https://mlsanity.com/paper/2402.01360/) (2024)
- [An Entity-based Claim Extraction Pipeline for Real-world Biomedical Fact-checking](https://mlsanity.com/paper/2304.05268/) (2023)
- [Towards Expectation Detection in Language: A Case Study on Treatment Expectations in Reddit](https://mlsanity.com/paper/2602.15504/) (2026)

## Abstract (arXiv)

Text mining and information extraction for the medical domain has focused on scientific text generated by researchers. However, their direct access to individual patient experiences or patient-doctor interactions can be limited. Information provided on social media, e.g., by patients and their relatives, complements the knowledge in scientific text. It reflects the patient's journey and their subjective perspective on the process of developing symptoms, being diagnosed and offered a treatment, being cured or learning to live with a medical condition. The value of this type of data is therefore twofold: Firstly, it offers direct access to people's perspectives. Secondly, it might cover information that is not available elsewhere, including self-treatment or self-diagnoses. Named entity recognition and relation extraction are methods to structure information that is available in unstructured text. However, existing medical social media corpora focused on a comparably small set of entities and relations and particular domains, rather than putting the patient into the center of analyses. With this paper we contribute a corpus with a rich set of annotation layers following the motivation to uncover and model patients' journeys and experiences in more detail. We label 14 entity classes (incl. environmental factors, diagnostics, biochemical processes, patients' quality-of-life descriptions, pathogens, medical conditions, and treatments) and 20 relation classes (e.g., prevents, influences, interactions, causes) most of which have not been considered before for social media data. The publicly available dataset consists of 2,100 tweets with approx. 6,000 entity and 3,000 relation annotations. In a corpus analysis we find that over 80 % of documents contain relevant entities. Over 50 % of tweets express relations which we consider essential for uncovering patients' narratives about their journeys.