Objective: In this study, we investigated the feasibility of leveraging different machine learning methods to help with clinical decision-making on orthodontic treatment planning, including the decision on extraction, extraction pattern and anchorage pattern.Setting and Sample Population: a group of 216 patients (156 extraction and 60 non-extraction) was enrolled.Materials and Methods: 32 input features were identified and used as the input variable. We proposed 7 machine learning methods including Logistic Regression, SVM, Decision Tree, Random Forest, Gaussian NB, KNN Classifier and Neural Network for extraction and 2 methods including Random forest and Neural Network for extraction and anchorage pattern.Results: For extraction decision, neural network yielded the most promising results by 93 % accuracy followed by Logistic regression (86 % accuracy and 93% precision), SVM and Random Forest (83 % accuracy and 90% precision). Naïve Bayesian classifier and KNN classifier failed to produce accuracy within the acceptable range but Naïve Bayes demonstrated the highest recall score with 92 %. For the decision on extraction & anchorage pattern neural network proved to be more reliable with 89 % accuracy on extraction pattern and 81% accuracy on extraction& anchorage pattern. Random Forest classifier with 41% accuracy did not show satisfactory results for this task. The most important features for decision on extraction in this study were Inter incisal angle, crowding in mandible, U1-FH, L1-NB and crowding in maxilla.Conclusion: The results demonstrate that neural network can yield considerable accuracy in a medical diagnosis model. However, other algorithms such as logistic regression and random forest can also be considered for simpler tasks such as extraction.

Hospitals face high occupation rates resulting in a longer boarding time and more complex bed management. This task could be facilitated by anticipating the unscheduled admissions. We study the capability of information from French electronic health records of an emergency department (ED) to predict patient disposition decisions. We compare the performances of five learning models in predicting the admission of a patient visiting an emergency department and in predicting the patient’s place of admission at two progressive time points throughout the ED care process: triage and initial assessment. Medical and administrative data were retrospectively collected on 53,608 visits to the Groupe Hospitalier Bretagne Sud, France, from July 2020 to June 2021. Our best model achieve a ROC-AUC equal to 88% and F1-score equal to 75% for admission prediction. Regarding medical unit admission prediction, the global ROC-AUC equals to 87% and F1-score ranges from 38% to 77% for the four admission classes, i.e., intensive care unit (6% of the dataset), medicine units (45%), surgery units (13.2%), and observation unit (35.8%). A validation with a posterior dataset indicates constant results.