Generation of Natural-Language Textual Summaries from Longitudinal Clinical Records

Ayelet Goldstein, Yuval Shahar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations


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 holds also in other clinical domains. We have recently developed a prototype system, CliniText, which, given a time-oriented clinical database, and appropriate formal abstraction and summarization knowledge, combines the computational mechanisms of 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 our methodology, and the feasibility of providing a free-text summary of longitudinal electronic patient records, by generating summaries in two very different domains-Diabetes Management and Cardiothoracic surgery. In particular, we explain the process of generating a discharge summary of a patient who had undergone a Coronary Artery Bypass Graft operation, and a brief summary of the treatment of a diabetes patient for five years.

Original languageEnglish
Title of host publicationMEDINFO 2015
Subtitle of host publicationeHealth-Enabled Health - Proceedings of the 15th World Congress on Health and Biomedical Informatics
EditorsAndrew Georgiou, Indra Neil Sarkar, Paulo Mazzoncini de Azevedo Marques
PublisherIOS Press
Number of pages5
ISBN (Electronic)9781614995630
StatePublished - 1 Jan 2015
Event15th World Congress on Health and Biomedical Informatics, MEDINFO 2015 - Sao Paulo, Brazil
Duration: 19 Aug 201523 Aug 2015

Publication series

NameStudies in Health Technology and Informatics
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365


Conference15th World Congress on Health and Biomedical Informatics, MEDINFO 2015
CitySao Paulo


  • Knowledge Representation
  • Natural Language Generation
  • Summarization
  • Temporal Abstraction

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management


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