Implementation of a System for Intelligent Summarization of Longitudinal Clinical Records

  • Ayelet Goldstein
  • Yuval Shahar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8268)


Physicians are required to interpret, abstract and present in free-text large amounts of clinical data in their daily tasks. This is especially true for chronic-disease domains, but also in other clinical domains. In our previous work, we have suggested a general framework for performing this task, given a time-oriented clinical database, and appropriate formal abstraction and summarization knowledge. We have recently developed a prototype system, CliniText, which demonstrates our ideas. Our prototype combines knowledge-based temporal data abstraction, textual summarization, abduction, and natural-language generation techniques, to generate an intelligent textual summary of longitudinal clinical data. We demonstrate both our methodology, and the feasibility of providing a free-text summary of longitudinal electronic patient records, by generating a discharge summary of a patient from the MIMIC database, who had undergone a Coronary Artery Bypass Graft operation.


Discharge Summary Final Text Nasal Cannula Abductive Reasoning Natural Language Generation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Goldstein, A., Shahar, Y.: A Framework for Automated Knowledge-Based Textual Summarization of Longitudinal Medical Records. In: KR4HC Workshop, Tallinn, Estonia (2012)Google Scholar
  2. 2.
    Combi, C., Shahar, Y., Keravnou-Papailiou, E.: Temporal Information Systems in Medicine. Springer, New York (2010)CrossRefGoogle Scholar
  3. 3.
    Portet, F., Reiter, E., Hunter, J., Sripada, S.: Automatic generation of textual summaries from neonatal intensive care data. AI in Med., 227–236 (2007)Google Scholar
  4. 4.
    Shahar, Y.: A framework for knowledge-based temporal abstraction. AI 90, 79–133 (1997)zbMATHGoogle Scholar
  5. 5.
    Lavrač, N., Kononenko, I., Keravnou, E., Kukar, M., Zupan, B.: Intelligent data analysis for medical diagnosis: using machine learning and temporal abstraction. AI Communications 11, 191–218 (1999)Google Scholar
  6. 6.
    McDonald, D.D., Bolc, L.: Natural Language Generation Systems. Springer, New York (1988)CrossRefGoogle Scholar
  7. 7.
    Huske-Kraus, D.: Text generation in clinical medicine-a review. Meth. Info. Med. 42, 51–60 (2003)Google Scholar
  8. 8.
    Portet, F., et al.: Automatic generation of textual summaries from neonatal intensive care data. Artificial Intelligence (2009), doi:10.1016/j.artint.2008.12.002Google Scholar
  9. 9.
    Hatsek, A., Shahar, Y., Taieb-Maimon, M., Shalom, E., Klimov, D., Lunenfeld, E.: A scalable architecture for incremental specification and maintenance of procedural decision-support knowledge. Open. Med. Info., 255–277 (2010)Google Scholar
  10. 10.
    Boaz, D., Shahar, Y.: Idan: A distributed temporal-abstraction mediator for medical databases. AI in Medicine, 21–30 (2003)Google Scholar
  11. 11.
    Owens, D., Sox, H.: Biomedical Decision Making: Probabilistic Clinical Reasoning. In: Biomedical Informatics (Shortliffe and Cimino), ch. 3, pp. 80–129 (2006)Google Scholar
  12. 12.
  13. 13.
  14. 14.
    The Laboratory for Computational Physiology,

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ayelet Goldstein
    • 1
  • Yuval Shahar
    • 1
  1. 1.Ben Gurion University of the NegevBeer-ShevaIsrael

Personalised recommendations