Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2023 12:00 AM
End Date
7-1-2023 12:00 AM
Description
Inflammatory bowel disease (IBD) is usually classified into Crohn's disease (CD) or ulcerative colitis (UC). Inconclusive cases are diagnosed with IBD unclassified (IBD-U). In 2018, IBD patients shared their disease history on Twitter and signed their tweets with #MyIBDHistory. In this research, we analyzed those tweets and built a logistic regression classifier that predicts patients' IBD type. We constructed tabular classification features and assessed their importance using the regression coefficients and association rules. We identified key features that distinguished CD from UC and used the classifier to predict the disease type of IBD-U patients. Our results correlated with IBD-related research. The two most prominent features that tilted the classification towards CD were suffering from fistulas or nutrient deficiencies. We identified gender differences in disease perspective prior to diagnosis. The research shows that the personal information shared by patients on Twitter can enhance existing medical knowledge regarding their disease.
Recommended Citation
Stemmer, Maya; Ravid, Gilad; and Parmet, Yisrael, "#MyIBDHistory on Twitter: Identifying Disease Characteristics Using Personal Tweets" (2023). Hawaii International Conference on System Sciences 2023 (HICSS-56). 2.
https://aisel.aisnet.org/hicss-56/hc/chronic_diseases/2
#MyIBDHistory on Twitter: Identifying Disease Characteristics Using Personal Tweets
Online
Inflammatory bowel disease (IBD) is usually classified into Crohn's disease (CD) or ulcerative colitis (UC). Inconclusive cases are diagnosed with IBD unclassified (IBD-U). In 2018, IBD patients shared their disease history on Twitter and signed their tweets with #MyIBDHistory. In this research, we analyzed those tweets and built a logistic regression classifier that predicts patients' IBD type. We constructed tabular classification features and assessed their importance using the regression coefficients and association rules. We identified key features that distinguished CD from UC and used the classifier to predict the disease type of IBD-U patients. Our results correlated with IBD-related research. The two most prominent features that tilted the classification towards CD were suffering from fistulas or nutrient deficiencies. We identified gender differences in disease perspective prior to diagnosis. The research shows that the personal information shared by patients on Twitter can enhance existing medical knowledge regarding their disease.
https://aisel.aisnet.org/hicss-56/hc/chronic_diseases/2