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A proof-of-concept study of extracting patient histories for rare/intractable diseases from social media

  • Received : 2020.03.18
  • Accepted : 2020.06.18
  • Published : 2020.05.28

Abstract

The amount of content on social media platforms such as Twitter is expanding rapidly. Simultaneously, the lack of patient information seriously hinders the diagnosis and treatment of rare/intractable diseases. However, these patient communities are especially active on social media. Data from social media could serve as a source of patient-centric knowledge for these diseases complementary to the information collected in clinical settings and patient registries, and may also have potential for research use. To explore this question, we attempted to extract patient-centric knowledge from social media as a task for the 3-day Biomedical Linked Annotation Hackathon 6 (BLAH6). We selected amyotrophic lateral sclerosis and multiple sclerosis as use cases of rare and intractable diseases, respectively, and we extracted patient histories related to these health conditions from Twitter. Four diagnosed patients for each disease were selected. From the user timelines of these eight patients, we extracted tweets that might be related to health conditions. Based on our experiment, we show that our approach has considerable potential, although we identified problems that should be addressed in future attempts to mine information about rare/intractable diseases from Twitter.

Keywords

References

  1. Hays R, Daker-White G. The care.data consensus? A qualitative analysis of opinions expressed on Twitter. BMC Public Health 2015;15:838. https://doi.org/10.1186/s12889-015-2180-9
  2. Allen CG, Andersen B, Chambers DA, Groshek J, Roberts MC. Twitter use at the 2016 Conference on the Science of Dissemination and Implementation in Health: analyzing #DIScience16. Implement Sci 2018;13:34. https://doi.org/10.1186/s13012-018-0723-z
  3. Pemmaraju N, Utengen A, Gupta V, Kiladjian JJ, Mesa R, Thompson MA. Rare cancers and social media: analysis of Twitter metrics in the first 2 years of a rare-disease community for myeloproliferative neoplasms on social media-#MPNSM. Curr Hematol Malig Rep 2017;12:598-604. https://doi.org/10.1007/s11899-017-0421-y
  4. Pemmaraju N, Utengen A, Gupta V, Thompson MA, Lane AA. Analysis of first-year Twitter metrics of a rare disease community for blastic plasmacytoid dendritic cell neoplasm (BPDCN) on social media: #BPDCN. Curr Hematol Malig Rep 2017;12:592-597. https://doi.org/10.1007/s11899-017-0422-x
  5. Svenstrup D, Jorgensen HL, Winther O. Rare disease diagnosis: a review of web search, social media and large-scale data-mining approaches. Rare Dis 2015;3:e1083145. https://doi.org/10.1080/21675511.2015.1083145
  6. Schumacher KR, Stringer KA, Donohue JE, Yu S, Shaver A, Caruthers RL, et al. Social media methods for studying rare diseases. Pediatrics 2014;133:e1345-e1353. https://doi.org/10.1542/peds.2013-2966
  7. Kaufmann P, Pariser AR, Austin C. From scientific discovery to treatments for rare diseases: the view from the National Center for Advancing Translational Sciences - Office of Rare Diseases Research. Orphanet J Rare Dis 2018;13:196. https://doi.org/10.1186/s13023-018-0936-x
  8. Kerr K, McAneney H, Smyth LJ, Bailie C, McKee S, McKnight AJ. A scoping review and proposed workflow for multi-omic rare disease research. Orphanet J Rare Dis 2020;15:107. https://doi.org/10.1186/s13023-020-01376-x
  9. Subirats L, Reguera N, Banon AM, Gomez-Zuniga B, Minguillon J, Armayones M. Mining Facebook data of people with rare diseases: a content-based and temporal analysis. Int J Environ Res Public Health 2018;15:1877. https://doi.org/10.3390/ijerph15091877
  10. Klein AZ, Sarker A, Cai H, Weissenbacher D, Gonzalez-Hernandez G. Social media mining for birth defects research: a rule-based, bootstrapping approach to collecting data for rare health-related events on Twitter. J Biomed Inform 2018;87:68-78. https://doi.org/10.1016/j.jbi.2018.10.001
  11. Kohler S, Carmody L, Vasilevsky N, Jacobsen JO, Danis D, Gourdine JP, et al. Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources. Nucleic Acids Res 2019;47:D1018-D1027. https://doi.org/10.1093/nar/gky1105