Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/22609
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hunter, James | |
dc.contributor.author | Gatt, Albert | |
dc.contributor.author | Portet, Francois | |
dc.contributor.author | Reiter, Ehud | |
dc.contributor.author | Sripada, Somayajulu | |
dc.date.accessioned | 2017-10-16T08:11:57Z | |
dc.date.available | 2017-10-16T08:11:57Z | |
dc.date.issued | 2008 | |
dc.identifier.citation | Hunter, J., Gatt, A., Portet, F., Reiter, E., & Sripada, S. (2008). Using natural language generation technology to improve information flows in intensive care units.18th European Conference on Artificial Intelligence, Patras. 678-682. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar//handle/123456789/22609 | |
dc.description.abstract | 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. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | IOS Press | en_GB |
dc.rights | info:eu-repo/semantics/openAccess | en_GB |
dc.subject | Natural language processing (Computer science) | en_GB |
dc.subject | Corpora (Linguistics) | en_GB |
dc.subject | Linguistic analysis (Linguistics) | en_GB |
dc.subject | Reference (Linguistics) | en_GB |
dc.subject | Word (Linguistics) | en_GB |
dc.title | Using natural language generation technology to improve information flows in intensive care units | en_GB |
dc.type | conferenceObject | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder | en_GB |
dc.bibliographicCitation.conferencename | 18th European Conference on Artificial Intelligence | en_GB |
dc.bibliographicCitation.conferenceplace | Patras, Greece, 21-25/08/2008 | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
Appears in Collections: | Scholarly Works - InsLin |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
pais2008-data-to-text.pdf | 110.42 kB | Adobe PDF | View/Open |
Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.