โ Scribed by Carolyn Watters; Wanhong Zheng; Evangelos Milios
No coin nor oath required. For personal study only.
In this paper we describe recent work toprovide a filtering service for readers interested in medically related news articles from online news sources. The first task is to filter out the nonmedical news articles. The remaining articles, the medically related ones, are then assigned MeSH headings for context and then categorized further by intended audience level (medical expert, medically knowledgeable, no particular medical background needed). The effectiveness goals include both accuracy and efficiency. That is, the process must be robust and efficient enough to scan significant data sets dynamically for the user at the same time as provide accurate results. Our primary effectiveness goal is to provide high accuracy at the medical/nonmedical filtering step. The secondary concern is the effectiveness of the subsequent grouping of the medical articles into reader groups with MeSH contexts for each paper. While it is relatively easy for people to judge that an article is nonmedical or medical in content it is relatively difficult to judge that any given article is of interest to certain types of readers, based on the medical language used. Consequently the goal is not necessarily to remove articles of higher readership level but rather to provide more information for the reader.
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