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Prior efforts have shown that under certain situations, retrieval effectiveness may be improved via the use of data fusion techniques. Although these improvements have been observed from the fusion of result sets from several distinct information retrieval systems, it has often been thought that fusing different document retrieval strategies in a single(More)
We automatically extract adverse drug reactions (ADRs) from consumer reviews provided on various drug social media sites to identify adverse reactions not reported by the United States Food and Drug Administration (FDA) but touted by consumers. We utilize various lexicons, identify patterns, and generate a synonym set that includes variations of medical(More)
The potential benefits of mining social media to learn about adverse drug reactions (ADRs) are rapidly increasing with the increasing popularity of social media. Unknown ADRs have traditionally been discovered by expensive post-marketing trials, but recent work has suggested that some unknown ADRs may be discovered by analyzing social media. We propose(More)
Mobile SMS spam is on the rise and is a prevalent problem. While recent work has shown that simple machine learning techniques can distinguish between ham and spam with high accuracy, this paper explores the individual contributions of various textual features in the classification process. <i>Our results reveal the surprising finding that simple is(More)
One of the tasks a Clinical Decision Support (CDS) system is designed to solve is retrieving the most relevant and actionable literature for a given medical case report. In this work, we present a query reformulation approach that addresses the unique formulation of case reports, making them suitable to be used on a general purpose search engine.(More)
In this work, we emphasize how to merge and re-rank contextual suggestions from the open Web based on a user " s personal interests. We retrieve relevant results from the open Web by identifying context-independent queries, combining them with location information, and issuing the combined queries to multiple Web search engines. Our learning to rank model(More)
Online mental health forums provide users with an anonymous support platform that is facilitated by moderators responsible for finding and addressing critical posts, especially those related to self-harm. Given the seriousness of these posts, it is important that the mod-erators are able to locate these critical posts quickly in order to respond with timely(More)
Interest in medical data mining is growing rapidly as more health-related data becomes available online. We propose methods for extracting Adverse Drug Reactions (ADRs) from forum posts and linking extracted ADRs to the drugs that users claim are responsible for them. We evaluate our methodology using a corpus of annotated forum posts. We find that our ADR(More)
Extraction and interpretation of temporal information from clinical text is essential for clinical practitioners and researchers. SemEval 2016 Task 12 (Clinical TempEval) addressed this challenge using the THYME 1 corpus, a corpus of clinical narratives annotated with a schema based on TimeML 2 guidelines. We developed and evaluated approaches for:(More)