Comparing predictions made by a prediction model, clinical score, and physicians

Journal: Applied Clinical Informatics
ISSN: 1869-0327
Issue: Vol. 4: Issue 3 2013
Pages: 376-391

Comparing predictions made by a prediction model, clinical score, and physicians

Pediatric asthma exacerbations in the emergency department

Research Article

K. J. Farion (1, 2, 3), S. Wilk (3, 4), W. Michalowski (3), D. O’Sullivan (3, 5), J. Sayyad-Shirabad (6)

(1) Division of Emergency Medicine, Children’s Hospital of Eastern Ontario, 401 Smyth Rd., Ottawa, Ontario, K1H 8L1 Canada; (2) Departments of Pediatrics and Emergency Medicine, University of Ottawa, 401 Smyth Rd., Ottawa, Ontario, K1H 8L1 Canada; (3) MET Research Group, Telfer School of Management, University of Ottawa, 55 Laurier Ave. E., Ottawa, Ontario, K1N 6N5 Canada; (4) Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60–965 Poznan, Poland; (5) Center for Health Informatics, School of Informatics, City University London, Northampton Square, London, EC1V 0HB, United Kingdom; (6) School of Electrical Engineering and Computer Science, University of Ottawa, 800 King Edward Ave., Ottawa, Ontario, K1N 6N5, Canada


Decision Support Techniques, Asthma, Pediatrics, emergency medical services


Background: Asthma exacerbations are one of the most common medical reasons for children to be brought to the hospital emergency department (ED). Various prediction models have been proposed to support diagnosis of exacerbations and evaluation of their severity.

Objectives: First, to evaluate prediction models constructed from data using machine learning techniques and to select the best performing model. Second, to compare predictions from the selected model with predictions from the Pediatric Respiratory Assessment Measure (PRAM) score, and predictions made by ED physicians.

Design: A two-phase study conducted in the ED of an academic pediatric hospital. In phase 1 data collected prospectively using paper forms was used to construct and evaluate five prediction models, and the best performing model was selected. In phase 2 data collected prospectively using a mobile system was used to compare the predictions of the selected prediction model with those from PRAM and ED physicians.

Measurements: Area under the receiver operating characteristic curve and accuracy in phase 1; accuracy, sensitivity, specificity, positive and negative predictive values in phase 2. Results: In phase 1 prediction models were derived from a data set of 240 patients and evaluated using 10-fold cross validation. A naive Bayes (NB) model demonstrated the best performance and it was selected for phase 2. Evaluation in phase 2 was conducted on data from 82 patients. Predictions made by the NB model were less accurate than the PRAM score and physicians (accuracy of 70.7%, 73.2% and 78.0% respectively), however, according to McNemar’s test it is not possible to conclude that the differences between predictions are statistically significant.

Conclusion: Both the PRAM score and the NB model were less accurate than physicians. The NB model can handle incomplete patient data and as such may complement the PRAM score. However, it requires further research to improve its accuracy.

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