Many challenges arise in evaluating predictions from dynamical models of physiological signals. A fundamental objective desired by clinicians is to predict regions of clinical interest rather than just match these signals. Although threshold based metrics provide much better insight by classifying predictions based on their ability to capture critical levels of abnormality, these metrics lack the ability to well characterize the prediction performance near decision boundaries. This paper presents a probabilistic approach for evaluating predictions by incorporating prediction uncertainty into the evaluation process. A case study is presented for evaluating 20s and 60s ahead predictions of critical oxygen levels in the blood using Gaussian Process (GP) models. Results show significant improvement in the ability to characterize the performance in predicting critical desaturation events.
['Hisham ElMoaqet', 'Dawn M. Tilbury', 'Satya Krishna Ramachandran']
A Probabilistic Approach for Evaluating Predictions of Critical Levels in Physiological Time Series