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

Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022), pages 4439–4450 Marseille, 20-25 June 2022 © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0 4439 Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR) Amelie W¨uhrl, Roman Klinger Institut f¨ur Maschinelle Sprachverarbeitung, University of Stuttgart Pfaffenwaldring 5b, 70569 Stuttgart, Germany {amelie.wuehrl, roman.klinger}@ims.uni-stuttgart.de Abstract For a long time, 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 is sometimes limited. Information provided on social media, e.g., by patients and their relatives, complements the knowledge available 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 were focused on 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 ≈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. Keywords: social media health mining, biomedical information extraction, BioNLP, relation extraction 1. Introduction On social media, doctors, patients, concerned relatives or other laypeople frequently discuss medical information. Twitter posts for example contain opinions and recommendations about treatments, recounts of medical experiences, or hypotheses and assumptions about medical issues like in Figure 1. This information is by design centered around the patient. It is impacted by the patient’s journey and their subjective perspective on processes like developing symptoms, being diagnosed and offered a treatment, being cured or learning to live with a disease. This data offers direct access to people’s perspectives and covers information that is not available elsewhere, e.g., aspects that might not be considered important or difficult to assess in clinical settings. This includes, e.g., assessments of a patient’s quality of life (Table 1, Ex. 2 and 5), or which environmental factors people consider when talking about their health (Table 1, Ex. 3 and 4). At the same …

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