A proof-of-concept study of extracting patient histories for rare/intractable diseases from social media
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...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BioMed Central
2020-06-01
|
| Series: | Genomics & Informatics |
| Subjects: | |
| Online Access: | http://genominfo.org/upload/pdf/gi-2020-18-2-e17.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832573802381312000 |
|---|---|
| author | Atsuko Yamaguchi Núria Queralt-Rosinach |
| author_facet | Atsuko Yamaguchi Núria Queralt-Rosinach |
| author_sort | Atsuko Yamaguchi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-fad397f6e811423daa0adc4fbb2cc2a7 |
| institution | Kabale University |
| issn | 2234-0742 |
| language | English |
| publishDate | 2020-06-01 |
| publisher | BioMed Central |
| record_format | Article |
| series | Genomics & Informatics |
| spelling | doaj-art-fad397f6e811423daa0adc4fbb2cc2a72025-02-02T02:56:18ZengBioMed CentralGenomics & Informatics2234-07422020-06-01182e1710.5808/GI.2020.18.2.e17613A proof-of-concept study of extracting patient histories for rare/intractable diseases from social mediaAtsuko Yamaguchi0Núria Queralt-Rosinach1 Tokyo City University, Setagaya, Tokyo 157-0087, Japan Leiden University Medical Center, Leiden, 2333 ZA, The NetherlandsThe 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.http://genominfo.org/upload/pdf/gi-2020-18-2-e17.pdfintractable diseasesrare diseasessocial media mining |
| spellingShingle | Atsuko Yamaguchi Núria Queralt-Rosinach A proof-of-concept study of extracting patient histories for rare/intractable diseases from social media Genomics & Informatics intractable diseases rare diseases social media mining |
| title | A proof-of-concept study of extracting patient histories for rare/intractable diseases from social media |
| title_full | A proof-of-concept study of extracting patient histories for rare/intractable diseases from social media |
| title_fullStr | A proof-of-concept study of extracting patient histories for rare/intractable diseases from social media |
| title_full_unstemmed | A proof-of-concept study of extracting patient histories for rare/intractable diseases from social media |
| title_short | A proof-of-concept study of extracting patient histories for rare/intractable diseases from social media |
| title_sort | proof of concept study of extracting patient histories for rare intractable diseases from social media |
| topic | intractable diseases rare diseases social media mining |
| url | http://genominfo.org/upload/pdf/gi-2020-18-2-e17.pdf |
| work_keys_str_mv | AT atsukoyamaguchi aproofofconceptstudyofextractingpatienthistoriesforrareintractablediseasesfromsocialmedia AT nuriaqueraltrosinach aproofofconceptstudyofextractingpatienthistoriesforrareintractablediseasesfromsocialmedia AT atsukoyamaguchi proofofconceptstudyofextractingpatienthistoriesforrareintractablediseasesfromsocialmedia AT nuriaqueraltrosinach proofofconceptstudyofextractingpatienthistoriesforrareintractablediseasesfromsocialmedia |