Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
Reexamining the cluster hypothesis: scatter/gather on retrieval results
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Algorithms for the Longest Common Subsequence Problem
Journal of the ACM (JACM)
A vector space model for automatic indexing
Communications of the ACM
PERSIVAL, a system for personalized search and summarization over multimedia healthcare information
Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
Passage retrieval based on language models
Proceedings of the eleventh international conference on Information and knowledge management
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
MFCS '94 Proceedings of the 19th International Symposium on Mathematical Foundations of Computer Science 1994
Quantitative evaluation of passage retrieval algorithms for question answering
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
TextTiling: segmenting text into multi-paragraph subtopic passages
Computational Linguistics
Simple BM25 extension to multiple weighted fields
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Scalable discovery of hidden emails from large folders
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Answering clinical questions with role identification
BioMed '03 Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine - Volume 13
The role of knowledge in conceptual retrieval: a study in the domain of clinical medicine
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Answer extraction, semantic clustering, and extractive summarization for clinical question answering
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Journal of Biomedical Informatics
Answering Clinical Questions with Knowledge-Based and Statistical Techniques
Computational Linguistics
Searching in Medline: Query expansion and manual indexing evaluation
Information Processing and Management: an International Journal
Query-drift prevention for robust query expansion
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Exploring criteria for successful query expansion in the genomic domain
Information Retrieval
Evaluation of the clinical question answering presentation
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Customization in a unified framework for summarizing medical literature
Artificial Intelligence in Medicine
Exploring correlation between ROUGE and human evaluation on meeting summaries
IEEE Transactions on Audio, Speech, and Language Processing
Text classification for assisting moderators in online health communities
Journal of Biomedical Informatics
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Objective: Clinical questions are often long and complex and take many forms. We have built a clinical question answering system named AskHERMES to perform robust semantic analysis on complex clinical questions and output question-focused extractive summaries as answers. Design: This paper describes the system architecture and a preliminary evaluation of AskHERMES, which implements innovative approaches in question analysis, summarization, and answer presentation. Five types of resources were indexed in this system: MEDLINE abstracts, PubMed Central full-text articles, eMedicine documents, clinical guidelines and Wikipedia articles. Measurement: We compared the AskHERMES system with Google (Google and Google Scholar) and UpToDate and asked physicians to score the three systems by ease of use, quality of answer, time spent, and overall performance. Results: AskHERMES allows physicians to enter a question in a natural way with minimal query formulation and allows physicians to efficiently navigate among all the answer sentences to quickly meet their information needs. In contrast, physicians need to formulate queries to search for information in Google and UpToDate. The development of the AskHERMES system is still at an early stage, and the knowledge resource is limited compared with Google or UpToDate. Nevertheless, the evaluation results show that AskHERMES' performance is comparable to the other systems. In particular, when answering complex clinical questions, it demonstrates the potential to outperform both Google and UpToDate systems. Conclusions: AskHERMES, available at http://www.AskHERMES.org, has the potential to help physicians practice evidence-based medicine and improve the quality of patient care.