Article

Assisting consumer health information retrieval with query recommendations.

Department of Radiology, Decision Systems Group, Thorn 309, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA.
Journal of the American Medical Informatics Association (Impact Factor: 3.93). 01/2006; 13(1):80-90. DOI: 10.1197/jamia.M1820
Source: DBLP

ABSTRACT Health information retrieval (HIR) on the Internet has become an important practice for millions of people, many of whom have problems forming effective queries. We have developed and evaluated a tool to assist people in health-related query formation.
We developed the Health Information Query Assistant (HIQuA) system. The system suggests alternative/additional query terms related to the user's initial query that can be used as building blocks to construct a better, more specific query. The recommended terms are selected according to their semantic distance from the original query, which is calculated on the basis of concept co-occurrences in medical literature and log data as well as semantic relations in medical vocabularies.
An evaluation of the HIQuA system was conducted and a total of 213 subjects participated in the study. The subjects were randomized into 2 groups. One group was given query recommendations and the other was not. Each subject performed HIR for both a predefined and a self-defined task.
The study showed that providing HIQuA recommendations resulted in statistically significantly higher rates of successful queries (odds ratio = 1.66, 95% confidence interval = 1.16-2.38), although no statistically significant impact on user satisfaction or the users' ability to accomplish the predefined retrieval task was found.
Providing semantic-distance-based query recommendations can help consumers with query formation during HIR.

0 Bookmarks
 · 
111 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This report provides an overview of the field of Information Retrieval (IR) in health-care. It does not aim to introduce general concepts and theories of IR but to present and describe specific aspects of Health Information Retrieval (HIR). After a brief in-troduction to the more broader field of IR, the significance of HIR at current times is discussed. Specific characteristics of Health Information, its classification and the main existing representations for health concepts are described together with the main prod-ucts and services in the area (e.g.: databases of health bibliographic content, health specific search engines and others). Recent research work is discussed and the most active researchers, projects and research groups are also presented. Main organizations and journals are also identified.
    08/2008;
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Query expansion is a commonly used approach to improving search results. Specific expansion methods, however, are expected to have different results. We have developed three different expansion methods using knowledge derived from medical thesaurus, medical literature, and clinical notes. Since the three different sources each have strengths and weaknesses, we hypothesized that combining the three sources will lead to better retrieval performance. Evaluation was performed for the 3 different query expansion techniques and an ensemble method on two sets of clinical notes. 11-point interpolated average precisions, MAP, and P(10) scores were calculated which indicate that topic model based expansion has the best results and the predication method the worst. This finding points to the potential of the topic modeling methods as well as the challenge in integrating different knowledge sources.
    Hawaii International Conference on System Sciences, Wailea, Maui, HI USA; 01/2013
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Searching for medical information on the Web is becoming popular and important these days. However, medical search has its own unique requirements that are not well handled by existing medical Web search engines. In this paper, we present iMed, the first intelligent medical Web search engine that extensively uses domain-specific medical knowledge and questionnaire to facilitate ordinary Internet users to search for medical information. iMed uses several key techniques to improve its usability and the quality of search results. First, since ordinary Internet users often have difficulty in clearly describing their situations due to lack of medical background, iMed uses a questionnaire-based query interface to guide searchers to provide the most important information about their situations. Second, iMed uses medical knowledge to automatically form multiple queries from a searcher' answers to the questions. Using these queries to perform search can significantly improve the quality of search results. Third, iMed simultaneously returns diversified search results for the multiple queries. This greatly increases the probability of finding useful information. Lastly, iMed suggests diversified, related medical phrases for the multiple queries concurrently. These medical phrases are extracted from the MeSH ontology and can help searchers quickly digest search results and refine their queries. We evaluated iMed under a wide range of medical scenarios. The results show that iMed is effective and efficient for medical search.

Full-text (2 Sources)

Download
45 Downloads
Available from
Jun 3, 2014