Comparing predictions made by a prediction model, clinical score, and physicians Pediatric asthma exacerbations in the emergency department

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

Aide à la décision, contribution pour la prise en charge de l'asthme

Les travaux présentés dans ce mémoire abordent le problème de l'aide à la décision dans le cas particulier de la prise en charge de l'asthme. L'approche retenue repose sur l'utilisation du paradigme du raisonnement à partir de cas qui permet de résoudre un nouveau problème à partir d'un problème du passé déjà résolu, par analogie. Nous présentons ADEMA (Aide à la DEcision Médicale pour l'Asthme), un système de Raisonnement à partir de Cas pour la prise en charge de l'asthme. Ce système doit proposer une prise en charge(diagnostic et traitement) au médecin en fonction de la consultation en cours. Un modèle de cas est proposé pour représenter une consultation asthmatique et qui a été obtenu à l'aide de médecins et de techniques d'analyse de données. Une mesure de similarité basée sur la méthode MVDM a été développée, testée et incluse dans le système. Une approche originale pour la phase d'adaptation a été proposée. Enfin, nous avons développé une interface utilisateur pour le système.The works presented in this memoir deal with the problem of the decision support in the asthma health care. The approach we propose aims at using the Case-Based Reasoning paradigm that attempts to solve a new problem by adapting established solutions to similar problems. We present ADEMA, a Case-based Reasoning system for asthma health care. It must propose a solution (diagnosis and treatment) to the physician according the consultation in progress. A case model is proposed to represent an asthmatic consultation and which was obtained using physicians and from data analysis. A similarity metric based on MVDM method was developed, tested and included in the system. An original approach for the reuse step was proposed. Lastly, we developed a user interface for our Case-Based Reasoning system.ROUEN-BU Sciences (764512102) / SudocROUEN-BU Sciences Madrillet (765752101) / SudocSudocFranceF