Abstract
The small number of patients enrolled in clinical trials to test new drugs and the relatively short trial durations make it paramount to monitor drugs' effectiveness and risks after they are approved by the regulatory agency. A thorough evaluation of a drug's effectiveness, side effects, and social and economic influences can prevent serious health damage to the public and shed light on new drug discovery and development. Past research has examined spontaneous reporting systems and electronic health records systems as data sources to study medication outcomes. However, both data sources are not able to provide complete and unbiased pictures of patients' care, making it necessary to integrate new data sources, such as increasingly prevalent social media data. In this study, we compared and evaluated four social media sites, in terms of data coverage and quality using 11 disease-drug pairs of careful selection. We found some patients reported serendipitous new indications for the drugs they were using for comorbid conditions, which is truly valuable information for drug repositioning. We also identified five cases of informal use of English on social media that can be challenging for computers to process, including comparative sentiment, sarcasm, grammar errors, pronoun reference and semantic reference, and emoticons. Our study suggests that social media can be a complementary new data source for studying medication outcomes, and reliable natural language processing and text mining methods are needed to automatically mine social media data on a large scale.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 |
| Editors | Xindong Wu, Alexander Tuzhilin, Hui Xiong, Jennifer G. Dy, Charu Aggarwal, Zhi-Hua Zhou, Peng Cui |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 472-479 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781467384926 |
| DOIs | |
| State | Published - Jan 29 2016 |
| Externally published | Yes |
| Event | 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 - Atlantic City, United States Duration: Nov 14 2015 → Nov 17 2015 |
Other
| Other | 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 |
|---|---|
| Country | United States |
| City | Atlantic City |
| Period | 11/14/15 → 11/17/15 |
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Keywords
- health data acquisition
- medication outcomes
- social media
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Computer Science Applications
Cite this
A Content Analysis of Patient-Reported Medication Outcomes on Social Media. / Ru, Boshu; Harris, Kimberly; Yao, Lixia.
Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015. ed. / Xindong Wu; Alexander Tuzhilin; Hui Xiong; Jennifer G. Dy; Charu Aggarwal; Zhi-Hua Zhou; Peng Cui. Institute of Electrical and Electronics Engineers Inc., 2016. p. 472-479 7395706.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A Content Analysis of Patient-Reported Medication Outcomes on Social Media
AU - Ru, Boshu
AU - Harris, Kimberly
AU - Yao, Lixia
PY - 2016/1/29
Y1 - 2016/1/29
N2 - The small number of patients enrolled in clinical trials to test new drugs and the relatively short trial durations make it paramount to monitor drugs' effectiveness and risks after they are approved by the regulatory agency. A thorough evaluation of a drug's effectiveness, side effects, and social and economic influences can prevent serious health damage to the public and shed light on new drug discovery and development. Past research has examined spontaneous reporting systems and electronic health records systems as data sources to study medication outcomes. However, both data sources are not able to provide complete and unbiased pictures of patients' care, making it necessary to integrate new data sources, such as increasingly prevalent social media data. In this study, we compared and evaluated four social media sites, in terms of data coverage and quality using 11 disease-drug pairs of careful selection. We found some patients reported serendipitous new indications for the drugs they were using for comorbid conditions, which is truly valuable information for drug repositioning. We also identified five cases of informal use of English on social media that can be challenging for computers to process, including comparative sentiment, sarcasm, grammar errors, pronoun reference and semantic reference, and emoticons. Our study suggests that social media can be a complementary new data source for studying medication outcomes, and reliable natural language processing and text mining methods are needed to automatically mine social media data on a large scale.
AB - The small number of patients enrolled in clinical trials to test new drugs and the relatively short trial durations make it paramount to monitor drugs' effectiveness and risks after they are approved by the regulatory agency. A thorough evaluation of a drug's effectiveness, side effects, and social and economic influences can prevent serious health damage to the public and shed light on new drug discovery and development. Past research has examined spontaneous reporting systems and electronic health records systems as data sources to study medication outcomes. However, both data sources are not able to provide complete and unbiased pictures of patients' care, making it necessary to integrate new data sources, such as increasingly prevalent social media data. In this study, we compared and evaluated four social media sites, in terms of data coverage and quality using 11 disease-drug pairs of careful selection. We found some patients reported serendipitous new indications for the drugs they were using for comorbid conditions, which is truly valuable information for drug repositioning. We also identified five cases of informal use of English on social media that can be challenging for computers to process, including comparative sentiment, sarcasm, grammar errors, pronoun reference and semantic reference, and emoticons. Our study suggests that social media can be a complementary new data source for studying medication outcomes, and reliable natural language processing and text mining methods are needed to automatically mine social media data on a large scale.
KW - health data acquisition
KW - medication outcomes
KW - social media
UR - http://www.scopus.com/inward/record.url?scp=84964746515&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84964746515&partnerID=8YFLogxK
U2 - 10.1109/ICDMW.2015.150
DO - 10.1109/ICDMW.2015.150
M3 - Conference contribution
AN - SCOPUS:84964746515
SP - 472
EP - 479
BT - Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
A2 - Wu, Xindong
A2 - Tuzhilin, Alexander
A2 - Xiong, Hui
A2 - Dy, Jennifer G.
A2 - Aggarwal, Charu
A2 - Zhou, Zhi-Hua
A2 - Cui, Peng
PB - Institute of Electrical and Electronics Engineers Inc.
ER -