international journal of medical informatics 76S ( 2 0 0 7 ) S362–S368 journal homepage: www.intl.elsevierhealth.com/journals/ijmi Towards automated classification of intensive care nursing narratives Marketta Hiissa a,b,* , Tapio Pahikkala a,c , Hanna Suominen a,c , Tuija Lehtikunnas d,e , Barbro Back a,b , Helena Karsten a,c , Sanna Salanter ¨ a d , Tapio Salakoski a,c a Turku Centre for Computer Science, Joukahaisenkatu 3-5 B, 20520 Turku, Finland b ˚ Abo Akademi University, Department of Information Technologies, Turku, Finland c University of Turku, Department of Information Technology, Turku, Finland d University of Turku, Department of Nursing Science, Turku, Finland e Turku University Hospital, Turku, Finland article info Article history: Received 29 December 2006 Received in revised form 20 March 2007 Accepted 28 March 2007 Keywords: Computerized patient records Intensive care Natural language processing Nursing Nursing records 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. Objectives: Our objective is to address two issues related to designing an automated classi- fier: 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 , and the performance of the algorithm was measured using area under ROC curve (AUC). Results: On average, the values of  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 auto- matically classified and this can offer a way to develop electronic patient records. © 2007 Elsevier Ireland Ltd. All rights reserved. 1. Introduction During the past years, health-care providers have been chang- ing paper-based patient records to electronic ones. This has, on the one hand, made more data available on each patient, but on the other hand, also offered new possibilities to utilize the gathered data. However, the effects of this switch have not ∗ Corresponding author. Tel.: +358 2 215 4078; fax: +358 2 241 0154. E-mail address: marketta.hiissa@abo.fi (M. Hiissa). only been positive. It has been found that electronic charting may not provide nurses with more time for tasks unrelated to manipulating data [1,2] and that electronic systems sup- port nurses in gathering information, but not in the active utilization of it [3]. In Finland, most of the intensive care nursing documenta- tion, i.e. documentation about the state of the patient, goals for 1386-5056/$ – see front matter © 2007 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.ijmedinf.2007.03.003