Building natural language generation systems
Building natural language generation systems
International Journal of Human-Computer Studies
The Evaluation of a Personalised Health Information System for Patients with Cancer
User Modeling and User-Adapted Interaction
Design of a knowledge-based report generator
ACL '83 Proceedings of the 21st annual meeting on Association for Computational Linguistics
Summarizing neonatal time series data
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 2
Suregen-2: a shell system for the generation of clinical documents
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 2
Choosing the content of textual summaries of large time-series data sets
Natural Language Engineering
Linguistic Interpretations of Scuba Dive Computer Data
IV '07 Proceedings of the 11th International Conference Information Visualization
Using the journalistic metaphor to design user interfaces that explain sensor data
INTERACT'11 Proceedings of the 13th IFIP TC 13 international conference on Human-computer interaction - Volume Part III
Expert Systems with Applications: An International Journal
Generating automated news to explain the meaning of sensor data
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
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In the drive to improve patient safety, patients in modern intensive care units are closely monitored with the generation of very large volumes of data. Unless the data are further processed, it is difficult for medical and nursing staff to assimilate what is important. It has been demonstrated that data summarization in natural language has the potential to improve clinical decision making; we have implemented and evaluated a prototype system which generates such textual summaries automatically. Our evaluation of the computer generated summaries showed that the decisions made by medical and nursing staff after reading the summaries were as good as those made after viewing the currently available graphical presentations with the same information content. Since our automatically generated textual summaries can be improved by including additional content and expert knowledge, they promise to enhance information exchange between the medical and nursing staff, particularly when integrated with the currently available graphical presentations. The main feature of this technology is that it brings together a diverse set of techniques such as medical signal analysis, knowledge based reasoning, medical ontology and natural language generation. In this paper we discuss the main components of our approach with a critical analysis of their strengths and limitations and present options for improvement to address these limitations.