Incorporating temporal EHR data in predictive models for risk stratification of renal function deterioration.

Predictive models built using temporal data in electronic health records (EHRs) can potentially play a major role in improving management of chronic diseases. However, these data present a multitude of technical challenges, including irregular sampling of data and varying length of available patient history. In this paper, we describe and evaluate three different approaches that use machine learning to build predictive models using temporal EHR data of a patient. The first approach is a commonly used non-temporal approach that aggregates values of the predictors in the patient's medical history. The other two approaches exploit the temporal dynamics of the data. The two temporal approaches vary in how they model temporal information and handle missing data. Using data from the EHR of Mount Sinai Medical Center, we learned and evaluated the models in the context of predicting loss of estimated glomerular filtration rate (eGFR), the most common assessment of kidney function. Our results show that incorporating temporal information in patient's medical history can lead to better prediction of loss of kidney function. They also demonstrate that exactly how this information is incorporated is important. In particular, our results demonstrate that the relative importance of different predictors varies over time, and that using multi-task learning to account for this is an appropriate way to robustly capture the temporal dynamics in EHR data. Using a case study, we also demonstrate how the multi-task learning based model can yield predictive models with better performance for identifying patients at high risk of short-term loss of kidney function.

A comparison of non-symmetric entropy-based classification trees and support vector machine for cardiovascular risk stratification.

Classification tree-based risk stratification models generate easily interpretable classification rules. This feature makes classification tree-based models appealing for use in a clinical setting, provided that they have comparable accuracy to other methods. In this paper, we present and evaluate the performance of a non-symmetric entropy-based classification tree algorithm. The algorithm is designed to accommodate class imbalance found in many medical datasets. We evaluate the performance of this algorithm, and compare it to that of SVM-based classifiers, when applied to 4219 non-ST elevation acute coronary syndrome patients. We generated SVM-based classifiers using three different strategies for handling class imbalance: cost-sensitive SVM learning, synthetic minority oversampling (SMOTE), and random majority undersampling. We used both linear and radial basis kernel-based SVMs. Our classification tree models outperformed SVM-based classifiers generated using each of the three techniques. On average, the classification tree models yielded a 14% improvement in G-score and a 21% improvement in F-score relative to the linear SVM classifiers with the best performance. Similarly, our classification tree models yielded a 12% improvement in G-score and a 21% improvement in the F-score over the best RBF kernel-based SVM classifiers.