The contribution of nursing data to the development of a predictive model for the detection of acute pancreatitis.
The increasing use of information system has resulted in the accumulation of a large volume of nursing data in electronic medical records. These data have great potential for supporting the various clinical decisions made by physicians, nurses, and managers. However, how to re-use of nursing data remains largely an issue of informatics. The aim of this study was to demonstrate how these nursing data can be used and how much they could contribute to developing a predictive model for an expert system for early detection of acute pancreatitis. We employed a probability-based model consisting of a Bayesian network and trained this model with the patient data retrospectively retrieved from the enterprise data warehouse of a tertiary hospital. The performance of the predictive model was measured based on the error rate and the area under receiver operating characteristics curve, which were 13.89 % and 0.93, respectively. The sensitivity of the acute pancreatitis to the findings from each nursing data was measured using a test of sensitivity. The results showed that the role of nursing data is as important as laboratory data in formulating a model for an expert system.
Cho
In Sook
IS
Nursing Department, Inha University, Incheon, Korea. insook.cho@inha.ac.kr
Haug
Peter J
PJ
D000208:Acute Disease; D003625:Data Collection; D003936:Diagnosis, Computer-Assisted / Q000451:nursing*; D005544:Forecasting; D006801:Humans; D008962:Models, Theoretical; D010195:Pancreatitis / Q000175:diagnosis*; D014589:Utah