Analysis of ADR-Disease Association based on
Social Media Data
Mengnan Zhao, Christopher Yang
Introduction
Research Methods
Adverse drug reaction (ADR) represents a serious problem
worldwide and the detection of associations between ADRs
and diseases has been of great importance in healthcare. Most
of current studies focus on the relationship between ADRs and
drugs, while ADR-disease association plays a significant role in:
We constructed a health heterogeneous network and applied three
methods to analyze ADR-disease associations.
College of
Computing & Informatics
Vocabulary Analysis
(1) Medication use.Patients who experience ADRs usually want
to find alternative medicines, but if the ADR is strongly
correlated with their disease, it is probably be difficult to find
alternative drugs without that ADR. Hence, the awareness of
ADR-disease correlations is beneficial in medicine suggestion
and will resolve lots of health consumer concerns.
(2) Drug repositioning. Drug repositioning targets at discovering
potential uses of known drugs and is based on the discovering
of association between ADR X and disease D. Drugs showing X
while not yet indicated for D should be evaluated as a
candidate for disease D.
In addition, compared with traditional data sources of ADR,
social media data could provide more informative, timely and
available consumer-contributed contents.
DREXEL UNIVERSITY
(ADR lexicon, CHV)
Identification of ADRs from social
media data
Association Rules Mining
Method 1:
Asso(D-ADR) = lift(D-ADR)
ADR-Drug
Association
Disease-Drug
Association
Research questions
RQ1: Can we extract ADR signals from consumer-contributed
contents?
RQ2: Is it possible to detect ADR-disease associations based on
social media data?
Method 2:
Asso(D-ADR) = lift(D-R) × lift(R-ADR)
Method 3:
Asso(D-ADR) = Sum(lift(R-ADR))
ADR-Disease Association
Data Set
Results & Discussion
Conclusion
MedHelp, a health social media, was used to provided usergenerated information. To effectively detect the signals, we
targeted at 10 diseases that have more than 500 threads
(composed of a post and all the following comments) for each
of them.
Based on the value of links in the heterogeneous network, the
strength of disease-ADR associations are easily obtained. We then
ranked the 10 diseases to each ADR.
There exist strong associations between some diseases
and ADRs and these associations could be validated by
academic literatures.
Our methodology demonstrates the effectiveness of
applying social media data to detect medical signals
and ADR-disease associations.
Then according to disease-drug association information
provided by PharmGKB, a publicly available knowledge
database, 142 drugs indicated for the diseases were found.
We then looked for ADRs of that 142 drugs in SIDER database
and randomly selected 199 out of about 2,000 ADRs.
As a result, we collected more than 41,000 threads which
discuss 10 diseases and 142 drugs.
ADR: Abnormal
Sensation
Significant
diseases
Method 1
Myalgia
Method 2
Transplantation
Obsessive-Compulsive
Disorder
Method 3
Transplantation
Parkinson
ObsessiveCompulsive Disorder
Since there are no existing databases that provide direct ADRdisease association information, that is, no gold standard to verify
our results. We resorted to academic literature and did case studies
on five randomly chosen ADRs. And most of those associations are
well supported.
Future Works
Detection of ADR-disease associations lays the
groundwork of drug repositioning to some extent, so
we will focus on how to conduct drug repositioning
based on such associations in the future.