Mining social media data to investigate patient perceptions regarding DMARD pharmacotherapy for rheumatoid arthritis.

OBJECTIVES: We hypothesise that patients have a positive sentiment regarding biological/targeted synthetic disease modifying anti-rheumatic drugs (b/tsDMARDs) and a negative sentiment towards conventional synthetic agents (csDMARDs). We analysed discussions on social media platforms regarding DMARDs to understand the collective sentiment expressed towards these medications. METHODS: Treato analytics were used to download all available posts on social media about DMARDs in the context of rheumatoid arthritis. Strict filters ensured that user generated content was downloaded. The sentiment (positive or negative) expressed in these posts was analysed for each DMARD using sentiment analysis. We also analysed the reason(s) for this sentiment for each DMARD, looking specifically at efficacy and side effects. RESULTS: Computer algorithms analysed millions of social media posts and included 54 742 posts about DMARDs. We found that both classes had an overall positive sentiment. The ratio of positive to negative posts was higher for b/tsDMARDs (1.210) than for csDMARDs (1.048). Efficacy was the most commonly mentioned reason in posts with a positive sentiment and lack of efficacy was the most commonly mentioned reason for a negative sentiment. These were followed by the presence/absence of side effects in negative or positive posts, respectively. CONCLUSIONS: Public opinion on social media is generally positive about DMARDs. Lack of efficacy followed by side effects were the most common themes in posts with a negative sentiment. There are clear reasons why a DMARD generates a positive or negative sentiment, as the sentiment analysis technology becomes more refined, targeted studies could be done to analyse these reasons and allow clinicians to tailor DMARDs to match patient needs.

Mining social media data to assess the risk of skin and soft tissue infections from allergen immunotherapy.

BACKGROUND: Allergen immunotherapy (AIT) treatment for allergic rhinitis and asthma is used by 2.6 million Americans annually. Clinical and sterility testing studies identify no risk of contamination or infection from extracts prepared using recommended aseptic techniques, but regulatory concerns persist. Social media can be used to investigate rare adverse effects not captured by traditional studies. OBJECTIVE: We sought to investigate large social media databases for suggestion of AIT skin and soft tissue infection (SSTI) risk and compare this risk to a comparator procedure with a sterile pharmaceutical. METHODS: We analyzed US-restricted data from more than 10 common text-based social media platforms including Facebook, Twitter, and Reddit between 2012 and 2016. We used natural language processing (NLP) to identify posts related to AIT and, separately, influenza vaccination (comparator procedure). NLP was followed by manual review to identify posts suggesting a possible SSTI associated with either AIT or influenza vaccination. SSTI frequencies with 95% CIs were compared. RESULTS: We identified 25,126 AIT posts, which were matched by social media platform to 25,126 influenza vaccination-related posts. NLP identified 4088 (16.3%) AIT posts that required manual review, with 6 posts (0.02%; 95% CI, 0.005%-0.043%) indicative of possible AIT-related SSTI. NLP identified 2689 (10.7%) influenza posts that required manual review, with 7 posts (0.03%; 95% CI, 0.007%-0.048%) indicative of possible influenza vaccination-related SSTI. CONCLUSIONS: Social media data suggest that SSTI from AIT and influenza vaccination are equally rare events. Given that AIT's SSTI risk appears comparable to the risk using a sterile pharmaceutical based on social media data, current aseptic technique procedures seem safe.

Social media for arthritis-related comparative effectiveness and safety research and the impact of direct-to-consumer advertising.

BACKGROUND: Social media may complement traditional data sources to answer comparative effectiveness/safety questions after medication licensure. METHODS: The Treato platform was used to analyze all publicly available social media data including Facebook, blogs, and discussion boards for posts mentioning inflammatory arthritis (e.g. rheumatoid, psoriatic). Safety events were self-reported by patients and mapped to medical ontologies, resolving synonyms. Disease and symptom-related treatment indications were manually redacted. The units of analysis were unique terms in posts. Pre-specified conditions (e.g. herpes zoster (HZ)) were selected based upon safety signals from clinical trials and reported as pairwise odds ratios (ORs); drugs were compared with Fisher's exact test. Empirically identified events were analyzed using disproportionality analysis and reported as relative reporting ratios (RRRs). The accuracy of a natural language processing (NLP) classifier to identify cases of shingles associated with arthritis medications was assessed. RESULTS: As of October 2015, there were 785,656 arthritis-related posts. Posts were predominantly US posts (75%) from patient authors (87%) under 40 years of age (61%). For HZ posts (n = 1815), ORs were significantly increased with tofacitinib versus other rheumatoid arthritis therapies. ORs for mentions of perforated bowel (n = 13) were higher with tocilizumab versus other therapies. RRRs associated with tofacitinib were highest in conditions related to baldness and hair regrowth, infections and cancer. The NLP classifier had a positive predictive value of 91% to identify HZ. There was a threefold increase in posts following television direct-to-consumer advertisement (p = 0.04); posts expressing medication safety concerns were significantly more frequent than favorable posts. CONCLUSION: Social media is a challenging yet promising data source that may complement traditional approaches for comparative effectiveness research for new medications.

Clinicians' Reports in Electronic Health Records Versus Patients' Concerns in Social Media: A Pilot Study of Adverse Drug Reactions of Aspirin and Atorvastatin.

INTRODUCTION: Large databases of clinician reported (e.g., allergy repositories) and patient reported (e.g., social media) adverse drug reactions (ADRs) exist; however, whether patients and clinicians report the same concerns is not clear. OBJECTIVES: Our objective was to compare electronic health record data and social media data to better understand differences and similarities between clinician-reported ADRs and patients' concerns regarding aspirin and atorvastatin. METHODS: This pilot study explored a large repository of electronic health record data and social media data for clinician-reported ADRs and patients concerns for two common medications: aspirin (n = 31,817 ADRs accessible in clinical data; n = 19,186 potential ADRs accessible in social media data) and atorvastatin (n = 15,047 ADRs accessible in clinical data; n = 23,408 potential ADRs accessible in social media data). RESULTS: We found that the most frequently reported ADRs matched the most frequent patients' concerns. However, several less frequently reported reactions were more prevalent on social media (i.e., aspirin-induced hypoglycemia was discussed only on social media). Overall, we found a relatively strong positive and statistically significant correlation between the frequency ranking of reactions and patients' concerns for atorvastatin (Pearson's r = 0.61, p < 0.001) but not for aspirin (Pearson's r = 0.1, p = 0.69). CONCLUSION: Future studies should develop further natural language methods for a more detailed data analysis (i.e., identifying causality and temporal aspects in the social media data).

Balancing opioid-induced gastrointestinal side effects with pain management: Insights from the online community.

Opioids cause gastrointestinal (GI) symptoms such as nausea, vomiting, pain, and (in 40 percent) constipation that diminish patients' quality of life. Outside traditional surveys, little is known about the opioid-induced constipation (OIC) patient experience and its impact on pain management. The purpose of this study was to use data from social media platforms to qualitatively examine patient beliefs about OIC and other prominent GI side effects, their impact on effective pain management and doctor-patient interaction. The authors collected Tweets from March 25 to July 31, 2014, and e-forum posts from health-related social networking sites regardless of timestamp. The authors identified specific keywords related to opioids and GI side effects to locate relevant content in the dataset, which was then manually coded using ATLAS.ti software. The authors examined 2,519,868 Tweets and more than 1.8 billion e-forum posts, of which, 88,586 Tweets and 9,767 posts satisfied the search criteria. Three thousand three individuals experienced opioidinduced GI side effects, mostly related to phenanthrenes (n = 1,589), and 1,274 (42.4 percent) individuals described constipation. Over-the-counter medications and nonevidence-based natural approaches were most commonly used to alleviate constipation. Many individuals questioned, rotated, reduced, or stopped their opioid treatments as a result of their GI side effects. Investigation of social media reveals a struggle to balance pain management with opioid-induced GI side effects, especially constipation. Individuals are often unprepared to treat OIC, to modify opioid regiments without medical advice, and to resort to using natural remedies and treatments lacking scientific evidence of effectiveness. These results identify opportunities to improve physician-patient communication and explore effective treatment alternatives.

Experiments with hierarchical concept-based search.

Many digital libraries use hierarchical indexing schema, such as MeSH to enable concept based search in the retrieval phase. However, improving or outperforming the traditional full text search isn't trivial. We present an extensive set of experiments using a hierarchical concept based search retrieval method, applied in addition to several baselines, within the Vaidruya search and retrieval framework. Concept Based Search applied in addition to a low baseline is outperforming significantly, especially when queried on concepts in the third level and using disjunction within the hierarchical trees.