EAGER: Collaborative Research: CRUFS: A Unified Framework for Social Media Analysis of Adverse Drug Events

点击翻译按钮获取中文摘要

An adverse drug reaction (ADR) is any undesired response to a medication. ADRs have been linked with significant morbidity and mortality, accounting for as much as 5% of hospital admissions. 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 pre-approval 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. For psychotropic medications, the problem becomes compounded by the fact that most people with psychiatric diseases tend to have other health issues, with the individual taking multiple medications (both psychotropic and non-psychotropic) at the same time, with often unknown interactions between them. Given the huge quantities of data on drugs, drug interactions, and diseases, and the possibility offered by social media sources in obtaining more information about particular drugs and their side effects, the problem of post-marketing drug surveillance could be turned into a computational problem. This work will have relevance to government agencies charged with drug approval and disease monitoring (e.g., the Food and Drug Administration (FDA), Centers for Disease Control (CDC), public health agencies), pharmaceutical companies, and the general public. The proposed work will have impact beyond drug surveillance as the methods can be applied to other scenarios such as financial markets, national security, or other healthcare problems. Graduate and undergraduates students will be involved in the project, thereby gaining experience in doing research. Journal papers and conference presentations will be used to disseminate research results. The project takes a new approach to the problem of adverse drug event surveillance by relying heavily on the collective intelligence of the web community, with significant emphasis on social media and online sources. This calls for more serious attention to the ubiquity, veracity and diversity of data from these sources. Thus the general goal is to develop the CRUFS (credibility, recency, uniqueness, frequency and salience) framework as a uniform and innovative foundation for assessing different data channels in social media analysis of adverse drug events. The project will study methods to extract reliable signals from unreliable, noisy, redundant, and potentially deceptive online data, a core challenge in social media analytics. The project also proposes novel methods for ADR signal detection and signal fusion based on causality networks. The results will change the current passive surveillance that relies on voluntary reports, by making the public an integral part of a proactive drug surveillance system.