Health search engine with e-document analysis for reliable search results

Gaudinat, Arnaud; Ruch, Patrick; Joubert, Michel; Uziel, Philippe; Strauss, Anne; Thonnet, Michèle; Baud, Robert H.; Spahni, Stéphane; Weber, Patrick; Bonal, Juan; Boyer, Célia; Fieschi, Marius; Geissbühler, Antoine

doi:10.1016/j.ijmedinf.2005.11.002

Cited by 21 publications

(12 citation statements)

References 15 publications

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“…These sites assign quality scores to pages based on human review of their content; although importantly, they do not verify the correctness of any claims made on those sites. These labels, and other reliability signals, have been used for ranking within specialized Web sites [Gaudinat et al 2006] or to predict escalations in concern following the review of Web content [White and Horvitz 2010b].…”

Section: Quality Of Online Health Informationmentioning

confidence: 99%

Content Bias in Online Health Search

White

Hassan

2014

ACM Trans. Web

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Search engines help people answer consequential questions. Biases in retrieved and indexed content (e.g., skew toward erroneous outcomes that represent deviations from reality), coupled with searchers' biases in how they examine and interpret search results, can lead people to incorrect answers. In this article, we seek to better understand biases in search and retrieval, and in particular those affecting the accuracy of content in search results, including the search engine index, features used for ranking, and the formulation of search queries. Focusing on the important domain of online health search, this research broadens previous work on biases in search to examine the role of search systems in contributing to biases. To assess bias, we focus on questions about medical interventions and employ reliable ground truth data from authoritative medical sources. In the course of our study, we utilize large-scale log analysis using data from a popular Web search engine, deep probes of result lists on that search engine, and crowdsourced human judgments of search result captions and landing pages. Our findings reveal bias in results, amplifying searchers' existing biases that appear evident in their search activity. We also highlight significant bias in indexed content and show that specific ranking signals and specific query terms support bias. Both of these can degrade result accuracy and increase skewness in search results. Our analysis has implications for bias mitigation strategies in online search systems, and we offer recommendations for search providers based on our findings.

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Section: Quality Of Online Health Informationmentioning

confidence: 99%

Content Bias in Online Health Search

White

Hassan

2014

ACM Trans. Web

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“…Gaudinat et al [18] proposed a search engine for health documents. Hidola [3] is a personalized health and medical information search engine that allows searching personalized health information by selecting symptoms and answering questions in a medical questionnaire, rather than typing-in keywords.…”

Section: Related Workmentioning

confidence: 99%

Gauging the internet doctor

Vydiswaran

Zhai

Roth

2011

Proceedings of the 2011 Workshop on Data Mining for Medicine and Healthcare

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As more and more content is published and consumed online, it is imperative to know if an information nugget found on the Web is trustworthy or not. This is especially important for online medical information as it affects the most vulnerable group of users looking for medical help online. In this paper, we study the feasibility of automatically assessing the trustworthiness of a medical claim based on community knowledge, and propose techniques to assign a reliability score for an information nugget based on support over a community-generated collection. Specifically, we model the trustworthiness of a medical claim based on experiences shared by users in health forums and mailing lists. The proposed claim scores can be used to rank related claims on their relative trustworthiness. We further extend the notion of trustworthiness to a site (or equivalently, a database of claims from the site) and propose a scheme to rank sites based on aggregating the trust scores of claims from the site. Our experiments show that community knowledge can be exploited to help users distinguish reliable medical claims from unreliable ones. The proposed techniques can be applied to other domains where similar corpora are available.

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“…Nonetheless, the work of [27] uses word frequency based sorting to improve the ranking of suggestions generated by programs such as GNU Gspell and GNU Aspell. This method does not detect any misspellings nor generate suggestions but reports that Aspell gives better results than Gspell.…”

Section: Discussionmentioning

confidence: 99%

Matching health information seekers' queries to medical terms

et al. 2012

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BackgroundThe Internet is a major source of health information but most seekers are not familiar with medical vocabularies. Hence, their searches fail due to bad query formulation. Several methods have been proposed to improve information retrieval: query expansion, syntactic and semantic techniques or knowledge-based methods. However, it would be useful to clean those queries which are misspelled. In this paper, we propose a simple yet efficient method in order to correct misspellings of queries submitted by health information seekers to a medical online search tool.MethodsIn addition to query normalizations and exact phonetic term matching, we tested two approximate string comparators: the similarity score function of Stoilos and the normalized Levenshtein edit distance. We propose here to combine them to increase the number of matched medical terms in French. We first took a sample of query logs to determine the thresholds and processing times. In the second run, at a greater scale we tested different combinations of query normalizations before or after misspelling correction with the retained thresholds in the first run.ResultsAccording to the total number of suggestions (around 163, the number of the first sample of queries), at a threshold comparator score of 0.3, the normalized Levenshtein edit distance gave the highest F-Measure (88.15%) and at a threshold comparator score of 0.7, the Stoilos function gave the highest F-Measure (84.31%). By combining Levenshtein and Stoilos, the highest F-Measure (80.28%) is obtained with 0.2 and 0.7 thresholds respectively. However, queries are composed by several terms that may be combination of medical terms. The process of query normalization and segmentation is thus required. The highest F-Measure (64.18%) is obtained when this process is realized before spelling-correction.ConclusionsDespite the widely known high performance of the normalized edit distance of Levenshtein, we show in this paper that its combination with the Stoilos algorithm improved the results for misspelling correction of user queries. Accuracy is improved by combining spelling, phoneme-based information and string normalizations and segmentations into medical terms. These encouraging results have enabled the integration of this method into two projects funded by the French National Research Agency-Technologies for Health Care. The first aims to facilitate the coding process of clinical free texts contained in Electronic Health Records and discharge summaries, whereas the second aims at improving information retrieval through Electronic Health Records.

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Health search engine with e-document analysis for reliable search results

Cited by 21 publications

References 15 publications

Content Bias in Online Health Search

Content Bias in Online Health Search

Gauging the internet doctor

Matching health information seekers' queries to medical terms

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