Multiple Predictively Equivalent Risk Models for Handling Missing Data at Time of Prediction: with an Application in Severe Hypoglycemia Risk Prediction for Type 2 Diabetes Abstract   The presence of missing data at the time of prediction limits the application of risk models in clinical and research settings. Common ways of handling missing data at the time of prediction include measuring the missing value and employing statistical methods. Measuring missing value incurs additional cost, whereas previously reported statistical methods results in reduced performance compared to when all variables are measured. To tackle these challenges, we introduce a new strategy, the MMTOP algorithm (Multiple models for Missing values at Time Of Prediction), which does not require measuring additional data elements or data imputation. Specifically, at model construction time, the MMTOP constructs multiple predictively equivalent risk models utilizing different risk factor sets. The collection of models are stored and to be queried at prediction time. To predict an individualxe2x80x99s risk in the presence of incomplete data, the MMTOP selects the risk model based on measurement availability for that individual from the collection of predictively equivalent models and makes the risk prediction with the selected model. We illustrate the MMTOP with severe hypoglycemia (SH) risk prediction based on data from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study. We identified 77 predictively equivalent models for SH with crossvalidated cindex of 0.77xc2xb10.03. These models are based on 77 distinct risk factor sets containing 1217 risk factors. In terms of handling missing data at the time of prediction, the MMTOP outperforms all four tested competitor methods and maintains consistent performance as the number of missing variables increase.

Model Fusion to Enhance the Clinical Acceptability of LongTerm Glucose Predictions This paper presents the Derivatives Combination Predictor (DCP), a novel model fusion algorithm for making longterm glucose predictions for diabetic people. First, using the history of glucose predictions made by several models, the future glucose variation at a given horizon is predicted. Then, by accumulating the past predicted variations starting from a known glucose value, the fused glucose prediction is computed. A new loss function is introduced to make the DCP model learn to react faster to changes in glucose variations. The algorithm has been tested on 10 insilico type1 diabetic children from the T1DMS software. Three initial predictors have been used: a Gaussian process regressor, a feedforward neural network and an extreme learning machine model. The DCP and two other fusion algorithms have been evaluated at a prediction horizon of 120 minutes with the rootmeansquared error of the prediction, the rootmeansquared error of the predicted variation, and the continuous glucoseerror grid analysis. By making a successful tradeoff between prediction accuracy and predictedvariation accuracy, the DCP, alongside with its specifically designed loss function, improves the clinical acceptability of the predictions, and therefore the safety of the model for diabetic people.