Appendix: Content Summaries of Best Papers for the Education and Consumer Health Informatics
Section of the 2019 IMIA Yearbook
Abdellaoui R, Foulquié P, Texier N, Faviez C, Burgun A, Schück S
Detection of Cases of Noncompliance to Drug Treatment in Patient Forum Posts: Topic
Model Approach
J Med Internet Res 2018;20(3):e85
Non-compliance (or non-adherence) to long-term treatment is a worldwide problem detrimental
to the overall effectiveness of the health system. Social media holds a lot of promise
in improving communication and patient engagement. The example of benfluorex illustrates
how social media could be valuable sources for experts. Methods to identify messages
with adverse events mentions have been developed and it has been showed that social
media may even impact treatment adherence. The objective of this study was to evaluate
a topic model approach to detect patient non-compliant behaviours (dose change and
treatment cessation) associated with antidepressant drug (escitalopram) and antipsychotic
drug (aripiprazole) in online forums. Authors implemented a probabilistic topic model
to identify the topics that occurred in a corpus of messages mentioning these drugs,
posted from 2004 to 2013 on three of the most popular French forums. Around 6% (154/2691)
of online posts were detected on escitalopram non-compliance and 7% (122/1778) on
aripiprazole. The topic models approach detected cases of non-compliance behaviours
with average recall and precision scores of 98.5% (272/276) and 32.6% (272/844), respectively.
Authors concluded topic modelling was a valuable sensitive method to detect non-compliance.
However, it lacks specificity and manual review was required to distinguish between
true and false positives in each dataset. They suggested that syntactic and semantic
methods could be developed to recognize the experience, the temporal features, and
the object concerned by the action in the sentences.
Jones J, Pradhan M, Hosseini M, Kulanthaivel A, Hosseini M
Novel Approach to Cluster PatientGenerated Data Into Actionable Topics: Case Study
of a Web-Based Breast Cancer Forum
JMIR Med Inform 2018;6(4):e45
Despite the proliferation of social media use, such as blogs and forums, little is
known about the scope and quality of information shared, or the purposes that social
media sites serve for consumer decisional and support needs. This study explores approaches
for analysing the free-text social media data to discover hidden, less obvious, aspects
of health consumers’ lives and extract potential valuable information on managing
health and well being beyond the context of health care. This was applied to breast
cancer management and recovery in five online breast cancer forums (mainly breastcancer.org
community). Natural language processing and statistical modelling approach were used
to cluster >4 million postings into manageable topics. Topic modelling (cluster of
words that frequently occur together) was performed with the machine learning language
toolkit open source tool. It was followed by multiple linear regression analysis to
detect highly correlated topics among the different website forums. Quantitative content
analysis of the forums resulted in 20 categories of user discussion. Topic model organized
posts into 30 topics which were grouped into four distinct clusters of highly correlated
computationally modelled topics. These clusters were labelled “symptoms and diagnosis”,
“treatment”, “financial”, “friends and family”. Multiple regression analysis was performed
to identify the most significant topics discussed among the forum participants. They
were arranged in a descending order based on the Akaike information criterion value:
1) lingering side effects while in remission, 2) chemotherapy side effects and change
of treatment, 3) radiation and side effects, 4) genetic risk and testing, 5) support
from caregiver and medical team for long term recovery, and 6) looking for support
from people in similar circumstances.
Park A, Conway M, Chen AT
Examining Thematic Similarity, Difference, and Membership in Three Online Mental Health
Communities from Reddit: A Text Mining and Visualization Approach
Comput Human Behav 2018 Jan;78:98-112
Studies have consistently shown individuals can gain positive effects from interacting
with other individuals in similar circumstances. Online interactions have been shown
to improve depression, anxiety, stress, and negative mood, as well as to facilitate
coping and empowerment. Moreover, members of online health communities consistently
emphasize the benefits of participation with respect to their treatment decisions,
symptom management, clinical management, and outcomes. In this study, authors examine
the nature of online discussion (main themes expressed in the communities) and compare
issues (thematic overlap, similarity and differences among the communities) pertaining
to three mental health conditions: anxiety, depression, and post-traumatic stress
disorder (PTSD). The corpus was based on Reddit (http://www.reddit.com), a popular
social networking, online gathering, and news exchanging platform. Between the months
of Oct 2015 to Dec 2015, a total of 7,410 posts and 132,599 associated comments made
by 41,967 unique members were downloaded. Discussion themes were identified using
knowledge resources like Unified Medical Language System or clusters analysis. Similarity
among clusters in the network visualization used Louvain modularity algorithm. For
each of the three main themes (anxiety, depression, and PTSD), 15 clusters had been
generated. Using r/Anxiety subreddit discussion content, clusters including “social
anxiety”, “medication”, “school”, “panic attack”, and “therapy/therapist” contained
terms and labels which clearly differentiated the clusters from one another. A few
clusters, such as “positive emotion” and “gratitude” shared terms. For the r/Depression
subreddit, clusters including “birthday”, “school”, “sleep”, “work”, and “gratitude”
were clearly differentiated from one another. Clusters such as “talking to friends”
and “friends and family” shared identical or semantically similar terms. For the r/PTSD
subreddit, many clusters including “trauma therapy”, “work”, “sleep”, “trauma trigger”,
“EMDR therapy”, “nightmare”, “animal”, “research”, were clearly distinguishable. A
few clusters, such as “sleep” and “nightmare” shared similar terms but also had distinctive
and non-overlapping terms. Venn diagrams were built to summarize and highlight common
themes: “school” and “social related” between Anxiety Disorder and Depression, “living
with” between Anxiety Disorder and PTSD. The global intersection between the three
communities shared overlapping concerns and discussion patterns such as: “gratitude”,
“sleep”, “work” and “positive emotion”. However, Depression clusters focused on self-expressed
concerns (e.g., events associated with depressed moods), whereas Anxiety Disorders and Post-Traumatic
Stress Disorder clusters focused around treatment- and medication-related issues.
