III: Small: Collaborative Research: Social Media Based Analysis of Adverse Drug Events: User Modeling, Signal Reliability, and Signal Validation

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Adverse drug reactions (ADRs) have been associated with significant morbidity and mortality, and have been a significant cause of hospital admissions, accounting for as much as 5% of all admissions. About 2,000,000 serious ADRs are reported yearly in the US; 100,000 annual deaths are related to adverse drug events; serious ADRs rank 4th to 6th as causes of death. The problem stems from the fact that the ADR profile of a given drug is rarely complete at the time of official approval. The typically limited preapproval evaluation often results in the possibility that when the drug is finally approved for use in the general population (with significant diversity in race, gender, age, lifestyle), some previously unidentified ADRs are often observed. This problem is acute for psychotropic medications, given the fact that most people with psychiatric diseases tend to have other health issues, with the individual taking multiple drugs at the same time (both psychotropic and non-psychotropic), with often unknown interactions between them. Initial results have shown the promise of using social-media data for ADR signal detection. However, these methods are still faced with two critical challenges, namely, signal reliability and biological validation. Thus, this project proposes a detailed study on key determinants of signal reliability: credibility of social media sources, model of the users that generate source content, signal generation from such sources, and validation of the generated signals. This work will be relevant to government agencies charged with drug approval, drug monitoring, and disease monitoring, drug companies, hospitals, and the general public. The impact of the proposed work will go beyond drug surveillance, since the approaches proposed can be adapted for other healthcare problems, and for other scenarios, such as financial markets, and national security. Planned educational activities include outreach to high-school students, and involvement of undergraduate and graduate students. Research results will be disseminated via technical publications in professional journals and conference presentations. The project has three specific aims: (1) Enrich signal reliability in social media analysis of adverse drug events, using credibility analysis, user modeling and signal fusion via deep learning; (2) Signal validation via molecular level analysis; (3) Prototype development and evaluation. The ubiquity, veracity and diversity of data from various social media channels and other sources of user-generated content necessitate a serious consideration of their credibility, recency, uniqueness and salience. To enrich signal reliability, the team will propose novel methods for ADR signal detection using credibility analysis, and for user modeling and signal fusion based on deep leaning techniques. For signal validation, biological support for hypothesized ADRs, essentially connecting high-level observations from social media interactions to potential associations at molecular level networks and pathways, will be used. The results will change the current largely passive approach to post-marketing drug surveillance that relies heavily on voluntary reports, by ensuring reliability in social-media based approaches, thus making the public an integral part of a proactive drug surveillance system. The idea of signal fusion and deep learning for user modeling and signal generation can be extended for other uses beyond drug surveillance.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.