A1 Refereed original research article in a scientific journal
Relevance ranking of intensive care nursing narratives
Authors: Suominen H, Pahikkala T, Hiissa M, Lehtikunnas T, Back B, Karsten H, Salantera S, Salakoski T
Publication year: 2006
Journal:: Lecture Notes in Computer Science
Journal name in source: KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 1, PROCEEDINGS
Journal acronym: LECT NOTES ARTIF INT
Volume: 4251
First page : 720
Last page: 727
Number of pages: 8
ISBN: 3-540-46535-9
ISSN: 0302-9743
Abstract
Current computer-based patient records provide many capabilities to assist nurses' work in intensive care units, but the possibilities to utilize existing free-text documentation are limited without the appropriate tools. To ease this limitation, we present an adaptation of the Regularized Least-Squares (RLS) algorithm for ranking pieces of nursing notes with respect to their relevance to breathing, blood circulation, and pain. We assessed the ranking results by using Kendall's tau(b) as a measure of association between the output of the RLS algorithm and the desired ranking. The values of tau(b) were 0.62, 0.69, and 0.44 for breathing, blood circulation, and pain, respectively. These values indicate that a machine learning approach can successfully be used to rank nursing notes, and encourage further research on the use of ranking techniques when developing intelligent tools for the utilization of nursing narratives.
Current computer-based patient records provide many capabilities to assist nurses' work in intensive care units, but the possibilities to utilize existing free-text documentation are limited without the appropriate tools. To ease this limitation, we present an adaptation of the Regularized Least-Squares (RLS) algorithm for ranking pieces of nursing notes with respect to their relevance to breathing, blood circulation, and pain. We assessed the ranking results by using Kendall's tau(b) as a measure of association between the output of the RLS algorithm and the desired ranking. The values of tau(b) were 0.62, 0.69, and 0.44 for breathing, blood circulation, and pain, respectively. These values indicate that a machine learning approach can successfully be used to rank nursing notes, and encourage further research on the use of ranking techniques when developing intelligent tools for the utilization of nursing narratives.
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