A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
MedSearch: a specialized search engine for medical information
Proceedings of the 16th international conference on World Wide Web
MedSearch: a specialized search engine for medical information retrieval
Proceedings of the 17th ACM conference on Information and knowledge management
Evaluation of the clinical question answering presentation
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
A brief survey of computational approaches in social computing
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Intelligent personal health record: experience and open issues
Proceedings of the 1st ACM International Health Informatics Symposium
A learning support tool with clinical cases based on concept maps and medical entity recognition
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
Medical information retrieval: an instance of domain-specific search
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Computer Methods and Programs in Biomedicine
Toward a model of domain-specific search
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
MedRank: discovering influential medical treatments from literature by information network analysis
ADC '13 Proceedings of the Twenty-Fourth Australasian Database Conference - Volume 137
| Hi-index | 0.00 |
Searching for medical information on the Web has become highly popular, but it remains a challenging task because searchers are often uncertain about their exact medical situations and unfamiliar with medical terminology. To address this challenge, we have built an intelligent medical Web search engine called iMed, which uses medical knowledge and an interactive questionnaire to help searchers form queries. This paper focuses on iMed's iterative search advisor, which integrates medical and linguistic knowledge to help searchers improve search results iteratively. Such an iterative process is common for general Web search, and especially crucial for medical Web search, because searchers often miss desired search results due to their limited medical knowledge and the task's inherent difficulty. iMed's iterative search advisor helps the searcher in several ways. First, relevant symptoms and signs are automatically suggested based on the searcher's description of his situation. Second, instead of taking for granted the searcher's answers to the questions, iMed ranks and recommends alternative answers according to their likelihoods of being the correct answers. Third, related MeSH medical phrases are suggested to help the searcher refine his situation description. We demonstrate the effectiveness of iMed's iterative search advisor by evaluating it using real medical case records and USMLE medical exam questions.