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Relevance Ranking of Intensive Care Nursing Narratives

Hanna Suominen, Tapio Pahikkala, Marketta Hiissa, Tuija Lehtikunnas, Barbro Back, Helena Karsten, Sanna Salanterä, Tapio Salakoski, Relevance Ranking of Intensive Care Nursing Narratives. TUCS Technical Reports 740, Turku Centre for Computer Science, 2006.

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 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 recorded nursing narratives.

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BibTeX entry:

@TECHREPORT{tSuPaHiLeBaKaSaSa06a,
  title = {Relevance Ranking of Intensive Care Nursing Narratives},
  author = {Suominen, Hanna and Pahikkala, Tapio and Hiissa, Marketta and Lehtikunnas, Tuija and Back, Barbro and Karsten, Helena and Salanterä, Sanna and Salakoski, Tapio},
  number = {740},
  series = {TUCS Technical Reports},
  publisher = {Turku Centre for Computer Science},
  year = {2006},
  keywords = {Computerized Patient Records, Natural Language Processing, Nursing Documentation, Ranking},
  ISBN = {952-12-1683-2},
}

Belongs to TUCS Research Unit(s): Turku BioNLP Group, Data Mining and Knowledge Management Laboratory, Health and Medical Informatics Institute

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