Automatic Generation of Textual Summaries from Neonatal Intensive Care Data

Authors:
François Portet;Ehud Reiter;Jim Hunter;Somayajulu Sripada
Affiliations:
Department of Computing Science, University of Aberdeen, Aberdeen AB24 3UE, UK;Department of Computing Science, University of Aberdeen, Aberdeen AB24 3UE, UK;Department of Computing Science, University of Aberdeen, Aberdeen AB24 3UE, UK;Department of Computing Science, University of Aberdeen, Aberdeen AB24 3UE, UK
Venue:
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
Year:
2007

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Abstract

Intensive care is becoming increasingly complex. If mistakes are to be avoided, there is a need for the large amount of clinical data to be presented effectively to the medical staff. Although the most common approach is to present the data graphically, it has been shown that textual summarisation can lead to improved decision making. As the first step in the BabyTalk project, a prototype is being developed which will generate a textual summary of 45 minutes of continuous physiological signals and discrete events (e.g.: equipment settings and drug administration). Its architecture brings together techniques from the different areas of signal analysis, medical reasoning, and natural language generation. Although the current system is still being improved, it is powerful enough to generate meaningful texts containing the most relevant information. This prototype will be extended to summarize several hours of data and to include clinical interpretation.