title: Towards automated classification of intensive care nursing narratives.
abstract: BACKGROUND
  Nursing narratives are an important part of patient documentation, but the possibilities to utilize them in the direct care process are limited due to the lack of proper tools. One solution to facilitate the utilization of narrative data could be to classify them according to their content.
  
  
  OBJECTIVE
  Our objective is to address two issues related to designing an automated classifier: domain experts' agreement on the content of classes Breathing, Blood Circulation and Pain, as well as the ability of a machine-learning-based classifier to learn the classification patterns of the nurses.
  
  
  METHODS
  The data we used were a set of Finnish intensive care nursing narratives, and we used the regularized least-squares (RLS) algorithm for the automatic classification. The agreement of the nurses was assessed by using Cohen's kappa, and the performance of the algorithm was measured using area under ROC curve (AUC).
  
  
  RESULTS
  On average, the values of kappa were around 0.8. The agreement was highest in the class Blood Circulation, and lowest in the class Breathing. The RLS algorithm was able to learn the classification patterns of the three nurses on an acceptable level; the values of AUC were generally around 0.85.
  
  
  CONCLUSIONS
  Our results indicate that the free text in nursing documentation can be automatically classified and this can offer a way to develop electronic patient records.
authors.affiliation: Turku Centre for Computer Science, Joukahaisenkatu 3-5 B, 20520 Turku, Finland. marketta.hiissa@abo.fi
authors.forename: Marketta
authors.initials: M
authors.lastname: Hiissa
authors.forename: Tapio
authors.initials: T
authors.lastname: Pahikkala
authors.forename: Hanna
authors.initials: H
authors.lastname: Suominen
authors.forename: Tuija
authors.initials: T
authors.lastname: Lehtikunnas
authors.forename: Barbro
authors.initials: B
authors.lastname: Back
authors.forename: Helena
authors.initials: H
authors.lastname: Karsten
authors.forename: Sanna
authors.initials: S
authors.lastname: Salanterä
authors.forename: Tapio
authors.initials: T
authors.lastname: Salakoski
mesh_terms: D000465:Algorithms; D002965:Classification; D003422:Critical Care; D005387:Finland; D006801:Humans; D033262:Narration; D048108:Nursing Informatics; D012372:ROC Curve