Journal of Medical Systems

, 42:27 | Cite as

Automatic Decision Support for Clinical Diagnostic Literature Using Link Analysis in a Weighted Keyword Network

  • Shuqing Li
  • Ying Sun
  • Dagobert Soergel
Systems-level quality improvement
Part of the following topical collections:
  1. Systems-Level Quality Improvement


We present a novel approach to recommending articles from the medical literature that support clinical diagnostic decision-making, giving detailed descriptions of the associated ideas and principles. The specific goal is to retrieve biomedical articles that help answer questions of a specified type about a particular case. Based on the filtered keywords, MeSH(Medical Subject Headings) lexicon and the automatically extracted acronyms, the relationship between keywords and articles was built. The paper gives a detailed description of the process of by which keywords were measured and relevant articles identified based on link analysis in a weighted keywords network. Some important challenges identified in this study include the extraction of diagnosis-related keywords and a collection of valid sentences based on the keyword co-occurrence analysis and existing descriptions of symptoms. All data were taken from medical articles provided in the TREC (Text Retrieval Conference) clinical decision support track 2015. Ten standard topics and one demonstration topic were tested. In each case, a maximum of five articles with the highest relevance were returned. The total user satisfaction of 3.98 was 33% higher than average. The results also suggested that the smaller the number of results, the higher the average satisfaction. However, a few shortcomings were also revealed since medical literature recommendation for clinical diagnostic decision support is so complex a topic that it cannot be fully addressed through the semantic information carried solely by keywords in existing descriptions of symptoms. Nevertheless, the fact that these articles are actually relevant will no doubt inspire future research.


Literature recommendation service Clinical decision support Link analysis Keyword co-occurrence analysis 



This work was supported by the Chinese National Social Science Foundation 16BTQ030 (2016).


  1. 1.
    Simpson, M. S., Voorhees, E., and Hersh, W., Overview of the TREC 2014 clinical decision support track. In: Proceedings of the23rd Text Retrieval Conference (TREC 2014). National Institute of Standards and Technology (NIST), 2014.Google Scholar
  2. 2.
    Abrahamsson, E., Forni, T., Skeppstedt, M., and Kvist, M., Medical text simplification using synonym replacement: adapting assessment of word difficulty to a compounding language. In: Proceedings of the Workshop on Predicting & Improving Text Readability for Target Reader Populations (pp. 57–65). Association for Computational Linguistics, 2014.Google Scholar
  3. 3.
    Safran, C., Bloomrosen, M., Hammond, W.E., Labkoff, S., Markel-Fox, S., Tang, P.C., and Detmer, D.E., Toward a national framework for the secondary use of health data: An American medical informatics association white paper. Journal of the American Medical Informatics Association. 14(1):1–9, 2007.CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Elkin, P.L., Liebow, M., Bauer, B.A., Chaliki, S., Wahner-Roedler, D., Bundrick, J., et al., The introduction of a diagnostic decision support system (DXplain™) into the workflow of a teaching hospital service can decrease the cost of service for diagnostically challenging diagnostic related groups (DRGs). International Journal of Medical Informatics. 79(11):772–777, 2010.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Barnett, G.O., Cimino, J.J., Hupp, J.A., and Hoffer, E.P., DXplain: An evolving diagnostic decision-support system. The Journal of the American Medical Association. 258(1):67–74, 1987.CrossRefPubMedGoogle Scholar
  6. 6.
    Shwe, M.A., Middleton, B., Heckerman, D.E., Henrion, M., Horvitz, E.J., Lehmann, H.P., and Cooper, G.F., Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base. Methods of Information in Medicine. 30(4):241–255, 1991.PubMedGoogle Scholar
  7. 7.
    Klimov, D., and Shahar, Y., iALARM: An intelligent alert language for activation, response, and monitoring of medical alerts. In: Proceedings of Process Support and Knowledge Representation in Health Care (pp. 128–142). Springer International Publishing, 2013.Google Scholar
  8. 8.
    Elhadad, N., Kan, M.Y., Klavans, J.L., and McKeown, K.R., Customization in a unified framework for summarizing medical literature. Artificial Intelligence in Medicine. 33(2):179–198, 2005.CrossRefPubMedGoogle Scholar
  9. 9.
    Jaspers, M.W., Smeulers, M., Vermeulen, H., and Peute, L.W., Effects of clinical decision-support systems on practitioner performance and patient outcomes: A synthesis of high-quality systematic review findings. Journal of the American Medical Informatics Association. 18(3):327–334, 2011.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Seidling, H.M., Phansalkar, S., Seger, D.L., Paterno, M.D., Shaykevich, S., Haefeli, W.E., and Bates, D.W., Factors influencing alert acceptance: A novel approach for predicting the success of clinical decision support. Journal of the American Medical Informatics Association. 18(4):479–484, 2011.CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Wright, A., Sittig, D.F., Ash, J.S., Bates, D.W., Feblowitz, J., Fraser, G., et al., Governance for clinical decision support: Case studies and recommended practices from leading institutions. Journal of the American Medical Informatics Association. 18(2):187–194, 2011.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Wright, A., Sittig, D.F., Ash, J.S., Feblowitz, J., Meltzer, S., McMullen, C., et al., Development and evaluation of a comprehensive clinical decision support taxonomy: Comparison of front-end tools in commercial and internally developed electronic health record systems. Journal of the American Medical Informatics Association. 18(3):232–242, 2011.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Romano, M.J., and Stafford, R.S., Electronic health records and clinical decision support systems: Impact on national ambulatory care quality. Archives of Internal Medicine. 171(10):897–903, 2011.PubMedPubMedCentralGoogle Scholar
  14. 14.
    Hoeksema, L.J., Bazzy-Asaad, A., Lomotan, E.A., Edmonds, D.E., Ramírez-Garnica, G., Shiffman, R.N., and Horwitz, L.I., Accuracy of a computerized clinical decision-support system for asthma assessment and management. Journal of the American Medical Informatics Association. 18(3):243–250, 2011.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Raja, A.S., Ip, I.K., Prevedello, L.M., Sodickson, A.D., Farkas, C., Zane, R.D., et al., Effect of computerized clinical decision support on the use and yield of CT pulmonary angiography in the emergency department. Radiology. 262(2):468–474, 2012.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Tamine, L., and Chouquet, C., On the impact of domain expertise on query formulation, relevance assessment and retrieval performance in clinical settings. Information Processing & Management. 53(2):332–350, 2017.CrossRefGoogle Scholar
  17. 17.
    Abacha, A.B., and Zweigenbaum, P., MEANS: A medical question-answering system combining NLP techniques and semantic web technologies. Information Processing & Management. 51(5):570–594, 2015.CrossRefGoogle Scholar
  18. 18.
    Ryu, P.M., Jang, M.G., and Kim, H.K., Open domain question answering using Wikipedia-based knowledge model. Information Processing & Management. 50(5):683–692, 2014.CrossRefGoogle Scholar
  19. 19.
    Ou, S.Y., An entailment-based question answering method in a restricted domain. Journal of the China Society for Scientific and Technical Information. 30(5):540–547, 2011.Google Scholar
  20. 20.
    Amini, I., Martinez, D., Li, X., and Sanderson, M., Improving patient record search: A meta-data based approach. Information Processing & Management. 52(2):258–272, 2016.CrossRefGoogle Scholar
  21. 21.
    Demner-Fushman, D., Complex question answering based on a semantic domain model of clinical medicine. University of Maryland (United States), OCLC's Experimental Thesis Catalog. College Park, 2006.Google Scholar
  22. 22.
    Huang, X., Lin, J., and Demner-Fushman, D., Evaluation of PICO as a knowledge representation for clinical questions. In: Proceedings of AMIA Annual Symposium (pp. 359). American Medical Informatics Association, 2006.Google Scholar
  23. 23.
    Li, F., Han, S.J., and Zhang, D., The construction of sea cucumber disease diagnosis inference engine. Computer Applications and Software. 29(12):211–213, 2012.Google Scholar
  24. 24.
    Huang, Z.X., Zhong, C., and Li, X.R., Simulation study of respiratory disease diagnosis based on BP neural network. Journal of Hefei University of Technology (Natural Science). 35(3):347–349, 2012.Google Scholar
  25. 25.
    Li, S.Q., Xu, X., and Xu, M.J., The measures of books' recommending quality and personalized book recommendation service based on bipartite network of readers and books' lending relationship. Journal of Library Science in China. 39(3):83–95, 2013.Google Scholar
  26. 26.
    Giannis, N., Polykarpos, M., Nektarios, L., and Michalis, V., AUEB at TREC 2015: clinical decision support track. In: Proceedings of 24rd Text Retrieval Conference (TREC 2015). National Institute of Standards and Technology (NIST), 2015.Google Scholar
  27. 27.
    Jiang, J., Guan, Y., Su, J., Zhao, C., and Yang, J., HIT-WI at TREC 2015 Clinical Decision Support Track. In: Proceedings of 24rd Text Retrieval Conference (TREC 2015). National Institute of Standards and Technology (NIST), 2015.Google Scholar
  28. 28.
    Chen, W.Q., Lu, J.A., and Liang, J., Research in disease-gene network based on bipartite network projection. Complex Systems and Complexity Science. 6(1):13–19, 2009.Google Scholar
  29. 29.
    Li, S.Q., Research on automatic construction of domain ontology in library and information science based on weighted co-occurrence of citation keywords. Journal of the China Society for Scientific and Technical Information. 31(4):371–380, 2012.Google Scholar
  30. 30.
    Li, S.Q., Xu, X., Qian, G., and Han, W., A method for automatic recognition and visualization of main-paths in academic documents based on vibration algorithm and domain ontology. Journal of the China Society for Scientific and Technical Information. 31(7):676–685, 2012.Google Scholar
  31. 31.
    Liu, Y.H., and Wacholder, N., Evaluating the impact of MeSH (medical subject headings) terms on different types of searchers. Information Processing & Management. 53(4):851–870, 2017.CrossRefGoogle Scholar
  32. 32.
    Mu, X., Lu, K., and Ryu, H., Explicitly integrating MeSH thesaurus help into health information retrieval systems: An empirical user study. Information Processing & Management. 50(1):24–40, 2014.CrossRefGoogle Scholar
  33. 33.
    Kaur, J., and Gupta, V., Effective approaches for extraction of keywords. International Journal of Computer Science Issues. 7(6):144–148, 2010.Google Scholar
  34. 34.
    Zhou, W., Torvik, V.I., and Smalheiser, N.R., ADAM: Another database of abbreviations in MEDLINE. Bioinformatics. 22(22):2813–2818, 2006.CrossRefPubMedGoogle Scholar
  35. 35.
    Yilmaz, E., Kanoulas, E., and Aslam, J. A., A simple and efficient sampling method for estimating AP and NDCG. In: Proceedings of Engineering in International ACM SIGIR Conference on Research and Development in Information Retrieval (pp.603–610). ACM, 2008.Google Scholar
  36. 36.
    Roberts, K., Simpson, M. S., Voorhees, E. M., and Hersh, W. R., Overview of the TREC 2015 clinical decision support track. In: Proceedings of 24rd Text Retrieval Conference (TREC 2015). National Institute of Standards and Technology (NIST), 2015.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.College of Information EngineeringNanjing University of Finance & EconomicsNanjingChina
  2. 2.Department of Library and Information Studies, Graduate School of EducationUniversity at Buffalo, New York State UniversityBuffaloUSA

Personalised recommendations