@article{cao_askhermes_2011,
	title = {{AskHERMES}: {An} online question answering system for complex clinical questions},
	volume = {44},
	issn = {1532-0464},
	shorttitle = {{AskHERMES}},
	url = {https://www.sciencedirect.com/science/article/pii/S1532046411000062},
	doi = {10.1016/j.jbi.2011.01.004},
	abstract = {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.},
	language = {en},
	number = {2},
	urldate = {2021-04-13},
	journal = {Journal of Biomedical Informatics},
	author = {Cao, YongGang and Liu, Feifan and Simpson, Pippa and Antieau, Lamont and Bennett, Andrew and Cimino, James J. and Ely, John and Yu, Hong},
	month = apr,
	year = {2011},
	note = {ZSCC: 0000207},
	keywords = {Answer presentation, Clinical question answering, Passage retrieval, Question analysis, Summarization},
	pages = {277--288},
}

{"_id":"Xrk4fEsi4yyG9LoYb","bibbaseid":"cao-liu-simpson-antieau-bennett-cimino-ely-yu-askhermesanonlinequestionansweringsystemforcomplexclinicalquestions-2011","author_short":["Cao, Y.","Liu, F.","Simpson, P.","Antieau, L.","Bennett, A.","Cimino, J. J.","Ely, J.","Yu, H."],"bibdata":{"bibtype":"article","type":"article","title":"AskHERMES: An online question answering system for complex clinical questions","volume":"44","issn":"1532-0464","shorttitle":"AskHERMES","url":"https://www.sciencedirect.com/science/article/pii/S1532046411000062","doi":"10.1016/j.jbi.2011.01.004","abstract":"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.","language":"en","number":"2","urldate":"2021-04-13","journal":"Journal of Biomedical Informatics","author":[{"propositions":[],"lastnames":["Cao"],"firstnames":["YongGang"],"suffixes":[]},{"propositions":[],"lastnames":["Liu"],"firstnames":["Feifan"],"suffixes":[]},{"propositions":[],"lastnames":["Simpson"],"firstnames":["Pippa"],"suffixes":[]},{"propositions":[],"lastnames":["Antieau"],"firstnames":["Lamont"],"suffixes":[]},{"propositions":[],"lastnames":["Bennett"],"firstnames":["Andrew"],"suffixes":[]},{"propositions":[],"lastnames":["Cimino"],"firstnames":["James","J."],"suffixes":[]},{"propositions":[],"lastnames":["Ely"],"firstnames":["John"],"suffixes":[]},{"propositions":[],"lastnames":["Yu"],"firstnames":["Hong"],"suffixes":[]}],"month":"April","year":"2011","note":"ZSCC: 0000207","keywords":"Answer presentation, Clinical question answering, Passage retrieval, Question analysis, Summarization","pages":"277–288","bibtex":"@article{cao_askhermes_2011,\n\ttitle = {{AskHERMES}: {An} online question answering system for complex clinical questions},\n\tvolume = {44},\n\tissn = {1532-0464},\n\tshorttitle = {{AskHERMES}},\n\turl = {https://www.sciencedirect.com/science/article/pii/S1532046411000062},\n\tdoi = {10.1016/j.jbi.2011.01.004},\n\tabstract = {Objective\nClinical 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.\nDesign\nThis 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.\nMeasurement\nWe 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.\nResults\nAskHERMES 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.\nConclusions\nAskHERMES, available at http://www.AskHERMES.org, has the potential to help physicians practice evidence-based medicine and improve the quality of patient care.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2021-04-13},\n\tjournal = {Journal of Biomedical Informatics},\n\tauthor = {Cao, YongGang and Liu, Feifan and Simpson, Pippa and Antieau, Lamont and Bennett, Andrew and Cimino, James J. and Ely, John and Yu, Hong},\n\tmonth = apr,\n\tyear = {2011},\n\tnote = {ZSCC: 0000207},\n\tkeywords = {Answer presentation, Clinical question answering, Passage retrieval, Question analysis, Summarization},\n\tpages = {277--288},\n}\n\n\n\n\n\n\n\n","author_short":["Cao, Y.","Liu, F.","Simpson, P.","Antieau, L.","Bennett, A.","Cimino, J. 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