Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on sleep in brain health

LIVE: Thursday 2nd May 2024, 18:00-19:30 (CEST)

Quality sleep is essential for health. But what happens to our brains when sleep patterns are disturbed? Join our experts to explore the interplay between sleep disruption and neurological diseases, and the questions that you need to be asking your patients to help you prevent the harmful effects of sleep deprivation.
ADVANCED SEARCH
Log in
Top
BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2012

Open Access 01-12-2012 | Research article

Text summarization as a decision support aid

Authors: T Elizabeth Workman, Marcelo Fiszman, John F Hurdle

Published in: BMC Medical Informatics and Decision Making | Issue 1/2012

Login to get access

Abstract

Background

PubMed data potentially can provide decision support information, but PubMed was not exclusively designed to be a point-of-care tool. Natural language processing applications that summarize PubMed citations hold promise for extracting decision support information. The objective of this study was to evaluate the efficiency of a text summarization application called Semantic MEDLINE, enhanced with a novel dynamic summarization method, in identifying decision support data.

Methods

We downloaded PubMed citations addressing the prevention and drug treatment of four disease topics. We then processed the citations with Semantic MEDLINE, enhanced with the dynamic summarization method. We also processed the citations with a conventional summarization method, as well as with a baseline procedure. We evaluated the results using clinician-vetted reference standards built from recommendations in a commercial decision support product, DynaMed.

Results

For the drug treatment data, Semantic MEDLINE enhanced with dynamic summarization achieved average recall and precision scores of 0.848 and 0.377, while conventional summarization produced 0.583 average recall and 0.712 average precision, and the baseline method yielded average recall and precision values of 0.252 and 0.277. For the prevention data, Semantic MEDLINE enhanced with dynamic summarization achieved average recall and precision scores of 0.655 and 0.329. The baseline technique resulted in recall and precision scores of 0.269 and 0.247. No conventional Semantic MEDLINE method accommodating summarization for prevention exists.

Conclusion

Semantic MEDLINE with dynamic summarization outperformed conventional summarization in terms of recall, and outperformed the baseline method in both recall and precision. This new approach to text summarization demonstrates potential in identifying decision support data for multiple needs.
Appendix
Available only for authorised users
Literature
1.
Covell DG, Uman GC, Manning PR: Information needs in office practice: are they being met?. Ann Intern Med. 1985, 103 (4): 596-599.CrossRefPubMed
2.
Gorman PN, Helfand M: Information seeking in primary care: how physicians choose which clinical questions to pursue and which to leave unanswered. Med Decis Making. 1995, 15 (2): 113-119. 10.1177/0272989X9501500203.CrossRefPubMed
3.
Alper BS, Stevermer JJ, White DS, Ewigman BG: Answering family physicians' clinical questions using electronic medical databases. J Fam Pract. 2001, 50 (11): 960-965.PubMed
4.
Bergus GR, Randall CS, Sinift SD, Rosenthal DM: Does the structure of clinical questions affect the outcome of curbside consultations with specialty colleagues?. Arch Fam Med. 2000, 9 (6): 541-547. 10.1001/archfami.9.6.541.CrossRefPubMed
5.
Graber MA, Randles BD, Monahan J, Ely JW, Jennissen C, Peters B, Anderson D: What questions about patient care do physicians have during and after patient contact in the ED? The taxonomy of gaps in physician knowledge. Emerg Med J. 2007, 24 (10): 703-706. 10.1136/emj.2007.050674.CrossRefPubMedPubMedCentral
6.
Graber MA, Randles BD, Ely JW, Monnahan J: Answering clinical questions in the ED. Am J Emerg Med. 2008, 26 (2): 144-147. 10.1016/j.ajem.2007.03.031.CrossRefPubMed
7.
Ely JW, Osheroff JA, Chambliss ML, Ebell MH, Rosenbaum ME: Answering physicians' clinical questions: obstacles and potential solutions. J Am Med Inform Assoc. 2005, 12 (2): 217-224.CrossRefPubMedPubMedCentral
8.
Chambliss ML, Conley J: Answering clinical questions. J Fam Pract. 1996, 43 (2): 140-144.PubMed
9.
Golder S, McIntosh HM, Duffy S, Glanville J: Developing efficient search strategies to identify reports of adverse effects in MEDLINE and EMBASE. Health Info Libr J. 2006, 23 (1): 3-12. 10.1111/j.1471-1842.2006.00634.x.CrossRefPubMed
10.
Hersh WR, Hickam DH: How well do physicians use electronic information retrieval systems? A framework for investigation and systematic review. JAMA. 1998, 280 (15): 1347-1352. 10.1001/jama.280.15.1347.CrossRefPubMed
11.
Hoogendam A, Stalenhoef AF, Robbe PF, Overbeke AJ: Analysis of queries sent to PubMed at the point of care: observation of search behaviour in a medical teaching hospital. BMC Med Inform Decis Mak. 2008, 8: 42-10.1186/1472-6947-8-42.CrossRefPubMedPubMedCentral
12.
Kilicoglu H, Fiszman M, Rodriguez A, Shin D, Ripple A, Rindflesch TC: Semantic MEDLINE: A Web Application for Managing the Results of PubMed Searches. Proceedings of the Third International Symposium for Semantic Mining in Biomedicine:. 2008, 2008: 69-76.
13.
Mani I: Automatic summarization. 2001, John Benjamins Publishing Company, AmsterdamCrossRef
14.
Hahn U, Mani I: The challenges of automatic summarization. Computer. 2000, 33 (11): 29-36.CrossRef
15.
McKeown K, Elhadad N, Hatzivassiloglou V: 3rd ACM/IEEE-CS Joint Conference on Digital Libraries: 27-31 May 2003. Leveraging a Common Representation for Personalized Search and Summarization in a Medical Digital Library. Edited by: Lois Delcambre, Geneva Henry. 2003, IEEE Computer Society, , 159-170.
16.
Cao Y, Liu F, Simpson P, Antieau L, Bennett A, Cimino JJ, Ely J, Yu H: AskHERMES: An online question answering system for complex clinical questions. J Biomed Inform. 2011, 44 (2): 277-288. 10.1016/j.jbi.2011.01.004.CrossRefPubMedPubMedCentral
17.
Yang J, Cohen AM, Hersh W: Automatic summarization of mouse gene information by clustering and sentence extraction from MEDLINE abstracts. AMIA Annu Symp Proc. 2007, 831-835.
18.
Rindflesch TC, Fiszman M: The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text. J Biomed Inform. 2003, 36 (6): 462-477. 10.1016/j.jbi.2003.11.003.CrossRefPubMed
19.
20.
Rindflesch TC, Fiszman M, Libbus B: Medical Informatics: Knowledge Management and Data Mining in Biomedicine. Semantic Interpretation for the Biomedical Research Literature. Edited by: Chen H, Fuller S, Hersh W, Friedman C. 2005, Springer, New York, 399-422.
21.
Lindberg DA, Humphreys BL, McCray AT: The Unified Medical Language System. Methods Inf Med. 1993, 32 (4): 281-291.PubMed
22.
Takahashi K, Saga Y, Mizukami H, Takei Y, Machida S, Fujiwara H, Ozawa K, Suzuki M: Cetuximab inhibits growth, peritoneal dissemination, and lymph node and lung metastasis of endometrial cancer, and prolongs host survival. Int J Oncol. 2009, 35 (4): 725-729.PubMed
23.
Aronson AR: Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. Proc AMIA Symp. 2001, 17-21.
24.
Rindflesch TC: SemRep Predicates. 2012, Technical Report. Cognitive Sciences Branch, Lister Hill National Center for Biomedical Communications
25.
Fiszman M, Rindflesch TC, Kilicoglu H: Abstraction Summarization for Managing the Biomedical Research Literature. Proceedings of the HLT-NAACL Workshop on Computational Lexical Semantics. 2004, 2004:
26.
Fiszman M, Demner-Fushman D, Kilicoglu H, Rindflesch TC: Automatic summarization of MEDLINE citations for evidence-based medical treatment: a topic-oriented evaluation. J Biomed Inform. 2009, 42 (5): 801-813. 10.1016/j.jbi.2008.10.002.CrossRefPubMed
27.
Fiszman M, Rindflesch TC, Kilicoglu H: Summarizing drug information in Medline citations. AMIA Annu Symp Proc. 2006, 254-258.
28.
Sneiderman C, Demner-Fushman D, Fiszman M, Rosemblat G, Lang FM, Norwood D, Rindflesch TC: Semantic processing to enhance retrieval of diagnosis citations from Medline. AMIA Annu Symp Proc. 2006, 1104:
29.
Ahlers CB, Fiszman M, Demner-Fushman D, Lang FM, Rindflesch TC: Extracting semantic predications from Medline citations for pharmacogenomics. Pac Symp Biocomput. 2007, 209-220.
30.
Workman TE, Fiszman M, Hurdle JF, Rindflesch TC: Biomedical text summarization to support genetic database curation: Using Semantic MEDLINE to create a secondary database of genetic information. J Med Libr Assoc. 2010, 98 (4): 273-281. 10.3163/1536-5050.98.4.003.CrossRefPubMedPubMedCentral
31.
Zhang H, Fiszman M, Shin D, Miller CM, Rosemblat G, Rindflesch TC: Degree centrality for semantic abstraction summarization of therapeutic studies. J Biomed Inform. 2011, 44: 830-838. 10.1016/j.jbi.2011.05.001.CrossRefPubMedPubMedCentral
32.
Kullback S, Leibler RA: On information and sufficiency. Annals of Mathematical Statistics. 1951, 22 (1): 79-86. 10.1214/aoms/1177729694.CrossRef
33.
Resnik P: Selectional constraints: An information-theoretic model and its computational realization. Cognition. 1996, 61: 127-159. 10.1016/S0010-0277(96)00722-6.CrossRefPubMed
34.
Riloff E: Automatically Generating Extraction Patterns from Untagged Text. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence. 1996, 1044, 1049
35.
Riloff E, Phillips W: An introduction to the Sundance and Autoslog Systems. 2004, University of Utah School of, Computing
36.
Mani I: Maybury MT: Advances in automatic text summarization. 1999, MIT Press, Cambridge
37.
Banzi R, Liberati A, Moschetti I, Tagliabue L, Moja L: A review of online evidence- based practice point-of-care information summary providers. J Med Internet Res. 2010, 12 (3): e26-10.2196/jmir.1288.CrossRefPubMedPubMedCentral
38.
39.
40.
Khan NA, Hemmelgarn B, Padwal R, Larochelle P, Mahon JL, Lewanczuk RZ, McAlister FA, Rabkin SW, Hill MD, Feldman RD: The 2007 Canadian Hypertension Education Program recommendations for the management of hypertension: part 2 - therapy. Can J Cardiol. 2007, 23 (7): 539-550. 10.1016/S0828-282X(07)70798-5.CrossRefPubMedPubMedCentral
41.
Workman TE, Stoddart JM: Rethinking information delivery: using a natural language processing application for point-of-care data discovery. J Med Libr Assoc. 2012, 100 (2): 113-120. 10.3163/1536-5050.100.2.009.CrossRefPubMedPubMedCentral
42.
43.
44.
Fiszman M, Ortiz E, Bray B, Rindflesch T: Semantic processing to support clinical guideline development. AMIA Annu Symp Proc. 2008, 187: 191.
Metadata
Title
Text summarization as a decision support aid
Authors
T Elizabeth Workman
Marcelo Fiszman
John F Hurdle
Publication date
01-12-2012
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2012
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/1472-6947-12-41

Other articles of this Issue 1/2012

BMC Medical Informatics and Decision Making 1/2012 Go to the issue

Research article

Boundaries and e-health implementation in health and social care

Research article

High acceptability for cell phone text messages to improve communication of laboratory results with HIV-infected patients in rural Uganda: a cross-sectional survey study

Research article

A national survey of health service infrastructure and policy impacts on access to computerised CBT in Scotland

Correspondence

Evaluating genomic tests from bench to bedside: a practical framework

Correspondence

Requirements for guidelines systems: implementation challenges and lessons from existing software-engineering efforts

Research article

The freetext matching algorithm: a computer program to extract diagnoses and causes of death from unstructured text in electronic health records