SemScribe: Automatic Generation of Medical Reports

  • Lukas C. Faulstich
  • Kristin Irsig
  • Malik Atalla
  • Sebastian Varges
  • Heike Bieler
  • Manfred Stede
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7058)

Abstract

Images and videos resulting from diagnostic imaging procedures such as echocardiography need to be analyzed and interpreted by physicians in order to diagnose diseases of the patient. This process can be split into two steps: in a first step, various morphological features depicted in the images have to be interpreted and described. Then, a diagnostic conclusion has to be drawn from these observations. The first step can be facilitated by offering a structured entry form and some means to generate textual descriptions from the data entered in this form. While it is straight-forward to implement some basic text generation functionality using hard-wired text templates, the generation of fluent, well-readable text from structured data is much harder. In this collaboration we have combined advanced methods from computational linguistics and medical knowledge resources to solve this problem. We have built a prototype for the domain of echocardiography and evaluated it in a clinical setting.

Keywords

Noun Phrase Medical Report Text Generation Text Block 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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Lukas C. Faulstich
    • 1
  • Kristin Irsig
    • 1
  • Malik Atalla
    • 1
  • Sebastian Varges
    • 2
  • Heike Bieler
    • 2
  • Manfred Stede
    • 2
  1. 1.ID GmbH & Co. KGaABerlinGermany
  2. 2.Applied Computational Linguistics LabUniversity of PotsdamGermany

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