UNSUPERVISED INDEXING OF MEDLINE ARTICLES THROUGH GRAPH-BASED RANKING

The biomedical literature is extensively catalogued and indexed in MEDLINE. MEDLINE indexing is done by trained human indexers, who identify the most important concepts in each article, and is expensive and inconsistent. Automating the indexing task is difficult: the National Library of Medicine produces the Medical Text Indexer (MTI), which suggests potential indexing terms to the indexers. MTI’s output is not good enough to work unattended. In my thesis, I propose a different way to approach the indexing task called MEDRank. MEDRank creates graphs representing the concepts in biomedical articles and their relationships within the text, and applies graph-based ranking algorithms to identify the most important concepts in each article. I evaluate the performance of several automated indexing solutions, including my own, by comparing their output to the indexing terms selected by the human indexers. MEDRank outperformed all other evaluated indexing solutions, including MTI, in general indexing performance and precision. MEDRank can be used to cluster documents, index any kind of biomedical text with standard vocabularies, or could become part of MTI itself.

Using incomplete citation data for MEDLINE results ranking.

Information overload is a significant problem for modern medicine. Searching MEDLINE for common topics often retrieves more relevant documents than users can review. Therefore, we must identify documents that are not only relevant, but also important. Our system ranks articles using citation counts and the PageRank algorithm, incorporating data from the Science Citation Index. However, citation data is usually incomplete. Therefore, we explore the relationship between the quantity of citation information available to the system and the quality of the result ranking. Specifically, we test the ability of citation count and PageRank to identify "important articles" as defined by experts from large result sets with decreasing citation information. We found that PageRank performs better than simple citation counts, but both algorithms are surprisingly robust to information loss. We conclude that even an incomplete citation database is likely to be effective for importance ranking.

Using incomplete citation data for MEDLINE results ranking.

Information overload is a significant problem for modern medicine. Searching MEDLINE for common topics often retrieves more relevant documents than users can review. Therefore, we must identify documents that are not only relevant, but also important. Our system ranks articles using citation counts and the PageRank algorithm, incorporating data from the Science Citation Index. However, citation data is usually incomplete. Therefore, we explore the relationship between the quantity of citation information available to the system and the quality of the result ranking. Specifically, we test the ability of citation count and PageRank to identify "important articles" as defined by experts from large result sets with decreasing citation information. We found that PageRank performs better than simple citation counts, but both algorithms are surprisingly robust to information loss. We conclude that even an incomplete citation database is likely to be effective for importance ranking.

An evaluation of the Texas "A Woman's Right to Know" booklet: A state mandated patient education document for induced abortion

Many patient educational documents are written at a grade level higher than the level at which most individuals can read. This discrepancy can lead to treatment noncompliance and negative health outcomes. Therefore, it is important that patients receive readable health information. The Texas "A Woman's Right to Know" booklet is a state mandated informational document provided to women seeking abortion services. Given the significance of the abortion procedure, it is imperative that women considering having an abortion receive accurate and readable health materials. However, no published studies were found that evaluated the readability of the "A Woman's Right to Know" booklet. Therefore, the purpose of this study was to assess the readability of the "A Woman's Right to Know" booklet. To assess the readability, the Flesch-Kincaid readability test was used to evaluate the reading grade level of the entire "A Woman's Right to Know" booklet and each of the 7 sections of the booklet. The results showed that the readability of the entire booklet as well as each section of the booklet was written below the 8th grade reading level. Although the booklet was written below the estimated United States reading level (8th grade), the reading level of this booklet may still be too high for people in Texas who read below the 8th grade level. Based on these results, it is recommended that health care professionals involved in the distribution and explanation of the "A Woman's Right to Know" booklet provide their patients with both written and verbal medical information. The patients should be allowed to ask questions about the abortion procedure so that they can make the most informed choice.^