Beyond Information Retrieval—Medical Question Answering. Lee, M., Cimino, J., Zhu, H. R., Sable, C., Shanker, V., Ely, J., & Yu, H. AMIA Annual Symposium Proceedings, 2006:469–473, 2006. ![Beyond Information Retrieval—Medical Question Answering [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex Physicians have many questions when caring for patients, and frequently need to seek answers for their questions. Information retrieval systems (e.g., PubMed) typically return a list of documents in response to a user’s query. Frequently the number of returned documents is large and makes physicians’ information seeking “practical only ‘after hours’ and not in the clinical settings”. Question answering techniques are based on automatically analyzing thousands of electronic documents to generate short-text answers in response to clinical questions that are posed by physicians. The authors address physicians’ information needs and described the design, implementation, and evaluation of the medical question answering system (MedQA). Although our long term goal is to enable MedQA to answer all types of medical questions, currently, we currently implement MedQA to integrate information retrieval, extraction, and summarization techniques to automatically generate paragraph-level text for definitional questions (i.e., “What is X?”). MedQA can be accessed at http://www.dbmi.columbia.edu/~yuh9001/research/MedQA.html.
@article{lee_beyond_2006,
title = {Beyond {Information} {Retrieval}—{Medical} {Question} {Answering}},
volume = {2006},
issn = {1942-597X},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1839371/},
abstract = {Physicians have many questions when caring for patients, and frequently need to seek answers for their questions. Information retrieval systems (e.g., PubMed) typically return a list of documents in response to a user’s query. Frequently the number of returned documents is large and makes physicians’ information seeking “practical only ‘after hours’ and not in the clinical settings”. Question answering techniques are based on automatically analyzing thousands of electronic documents to generate short-text answers in response to clinical questions that are posed by physicians. The authors address physicians’ information needs and described the design, implementation, and evaluation of the medical question answering system (MedQA). Although our long term goal is to enable MedQA to answer all types of medical questions, currently, we currently implement MedQA to integrate information retrieval, extraction, and summarization techniques to automatically generate paragraph-level text for definitional questions (i.e., “What is X?”). MedQA can be accessed at http://www.dbmi.columbia.edu/{\textasciitilde}yuh9001/research/MedQA.html.},
urldate = {2020-05-15},
journal = {AMIA Annual Symposium Proceedings},
author = {Lee, Minsuk and Cimino, James and Zhu, Hai Ran and Sable, Carl and Shanker, Vijay and Ely, John and Yu, Hong},
year = {2006},
pmid = {17238385},
pmcid = {PMC1839371},
pages = {469--473},
}
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