A Biased Topic Modeling Approach for Case Control Study from Health Related Social Media Postings

abstract: Online social networks are the hubs of social activity in cyberspace, and using them to exchange knowledge, experiences, and opinions is common. In this work, an advanced topic modeling framework is designed to analyse complex longitudinal health information from social media with minimal human annotation, and Adverse Drug Events and Reaction (ADR) information is extracted and automatically processed by using a biased topic modeling method. This framework improves and extends existing topic modelling algorithms that incorporate background knowledge. Using this approach, background knowledge such as ADR terms and other biomedical knowledge can be incorporated during the text mining process, with scores which indicate the presence of ADR being generated. A case control study has been performed on a data set of twitter timelines of women that announced their pregnancy, the goals of the study is to compare the ADR risk of medication usage from each medication category during the pregnancy. In addition, to evaluate the prediction power of this approach, another important aspect of personalized medicine was addressed: the prediction of medication usage through the identification of risk groups. During the prediction process, the health information from Twitter timeline, such as diseases, symptoms, treatments, effects, and etc., is summarized by the topic modelling processes and the summarization results is used for prediction. Dimension reduction and topic similarity measurement are integrated into this framework for timeline classification and prediction. This work could be applied to provide guidelines for FDA drug risk categories. Currently, this process is done based on laboratory results and reported cases. Finally, a multi-dimensional text data warehouse (MTD) to manage the output from the topic modelling is proposed. Some attempts have been also made to incorporate topic structure (ontology) and the MTD hierarchy. Results demonstrate that proposed methods show promise and this system represents a low-cost approach for drug safety early warning.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Estimating time-to-onset of adverse drug reactions from spontaneous reporting databases.

International audienceBACKGROUND: Analyzing time-to-onset of adverse drug reactions from treatment exposure contributes to meeting pharmacovigilance objectives, i.e. identification and prevention. Post-marketing data are available from reporting systems. Times-to-onset from such databases are right-truncated because some patients who were exposed to the drug and who will eventually develop the adverse drug reaction may do it after the time of analysis and thus are not included in the data. Acknowledgment of the developments adapted to right-truncated data is not widespread and these methods have never been used in pharmacovigilance. We assess the use of appropriate methods as well as the consequences of not taking right truncation into account (naïve approach) on parametric maximum likelihood estimation of time-to-onset distribution. METHODS: Both approaches, naïve or taking right truncation into account, were compared with a simulation study. We used twelve scenarios for the exponential distribution and twenty-four for the Weibull and log-logistic distributions. These scenarios are defined by a set of parameters: the parameters of the time-to-onset distribution, the probability of this distribution falling within an observable values interval and the sample size. An application to reported lymphoma after anti TNF-¿ treatment from the French pharmacovigilance is presented. RESULTS: The simulation study shows that the bias and the mean squared error might in some instances be unacceptably large when right truncation is not considered while the truncation-based estimator shows always better and often satisfactory performances and the gap may be large. For the real dataset, the estimated expected time-to-onset leads to a minimum difference of 58 weeks between both approaches, which is not negligible. This difference is obtained for the Weibull model, under which the estimated probability of this distribution falling within an observable values interval is not far from 1. CONCLUSIONS: It is necessary to take right truncation into account for estimating time-to-onset of adverse drug reactions from spontaneous reporting databases

Automatic Filtering and Substantiation of Drug Safety Signals

Drug safety issues pose serious health threats to the population and constitute a major cause of mortality worldwide. Due to the prominent implications to both public health and the pharmaceutical industry, it is of great importance to unravel the molecular mechanisms by which an adverse drug reaction can be potentially elicited. These mechanisms can be investigated by placing the pharmaco-epidemiologically detected adverse drug reaction in an information-rich context and by exploiting all currently available biomedical knowledge to substantiate it. We present a computational framework for the biological annotation of potential adverse drug reactions. First, the proposed framework investigates previous evidences on the drug-event association in the context of biomedical literature (signal filtering). Then, it seeks to provide a biological explanation (signal substantiation) by exploring mechanistic connections that might explain why a drug produces a specific adverse reaction. The mechanistic connections include the activity of the drug, related compounds and drug metabolites on protein targets, the association of protein targets to clinical events, and the annotation of proteins (both protein targets and proteins associated with clinical events) to biological pathways. Hence, the workflows for signal filtering and substantiation integrate modules for literature and database mining, in silico drug-target profiling, and analyses based on gene-disease networks and biological pathways. Application examples of these workflows carried out on selected cases of drug safety signals are discussed. The methodology and workflows presented offer a novel approach to explore the molecular mechanisms underlying adverse drug reactions