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

Abstract

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.

Identifying Urinary Tract Infection-Related Information in Home Care Nursing Notes

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“…We used NimbleMiner to develop the NLP algorithms. Nimble-Miner is a user-driven text classification system previously applied in different domains, 20 , 21 including home care. 15 NimbleMiner includes several stages of clinical note processing that are briefly described here ( Figure 1 ), with more details provided in Topaz et al 22 …”

Free-Text Documentation of Dementia Symptoms in Home Healthcare: A Natural Language Processing Study

“…NimbleMiner was used to develop the NLP algorithms. NimbleMiner is an NLP system previously applied in several clinical domains ( Blumenthal et al, 2019 ; Topaz, Murga, Bar-Bachar, et al, 2019 ), including HHC ( Topaz, Murga, Gaddis, et al, 2019 ). NimbleMiner includes number of methodological stages of clinical text processing that are briefly described in the Supplementary material (Appendix A) .…”

The applications of eHealth technologies in the management of asthma and allergic diseases

“…The data were analysed using a patented algorithm that applied NLP to online free-text discussions in order to detect adverse events related to allergen immunotherapy. 116 Reddit, with its application programming interface, has become a widely studied social media platform for understanding opinions on health issues, especially among young people. 117 Image-based social media platforms, especially Instagram and Pinterest, deserve further research assessment, 118 while at the same time posing additional ethical issues.…”