TY - GEN
T1 - Identifying adverse drug events from health social media
T2 - 2nd International Conference for Smart Health, CSH 2014
AU - Liu, Xiao
AU - Liu, Jing
AU - Chen, Hsinchun
PY - 2014
Y1 - 2014
N2 - Health social media sites have emerged as major platforms for discussions of treatments and drug side effects, making them a promising source for listening to patients' voices in adverse drug event reporting. However, extracting patient adverse drug event reports from social media continues to be a challenge in health informatics research. To utilize the fertile health social media data for drug safety research, we develop advanced information extraction techniques for identifying adverse drug events in health social media. A case study is conducted on a heart disease discussion forum to evaluate the performance. Our approach achieves an f-measure of 82% in the recognition of medical events and treatments, an f-measure of 69% for identifying adverse drug events and an f-measure of 90% in patient report extraction. Analysis on the extracted adverse drug events suggests that health social media can provide supplemental information for adverse drug events and drug interactions. It provides a less biased insight into the distribution of adverse events among heart disease population compared to data from a drug regulatory agency.
AB - Health social media sites have emerged as major platforms for discussions of treatments and drug side effects, making them a promising source for listening to patients' voices in adverse drug event reporting. However, extracting patient adverse drug event reports from social media continues to be a challenge in health informatics research. To utilize the fertile health social media data for drug safety research, we develop advanced information extraction techniques for identifying adverse drug events in health social media. A case study is conducted on a heart disease discussion forum to evaluate the performance. Our approach achieves an f-measure of 82% in the recognition of medical events and treatments, an f-measure of 69% for identifying adverse drug events and an f-measure of 90% in patient report extraction. Analysis on the extracted adverse drug events suggests that health social media can provide supplemental information for adverse drug events and drug interactions. It provides a less biased insight into the distribution of adverse events among heart disease population compared to data from a drug regulatory agency.
KW - Adverse drug event extraction
KW - Health social media analytics
KW - Heart disease
KW - Medical entity extraction
KW - Statistical learning
UR - http://www.scopus.com/inward/record.url?scp=84905217429&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84905217429&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-08416-9_3
DO - 10.1007/978-3-319-08416-9_3
M3 - Conference contribution
AN - SCOPUS:84905217429
SN - 9783319084152
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 25
EP - 36
BT - Smart Health - International Conference, ICSH 2014, Proceedings
PB - Springer Verlag
Y2 - 10 July 2014 through 11 July 2014
ER -