Joint impact of clinical and behavioral variables on the risk of unplanned readmission and death after a heart failure hospitalization

Most current methods for modeling rehospitalization events in heart failure patients make use of only clinical and medications data that is available in the electronic health records. However, information about patient-reported functional limitations, behavioral variables and socio-economic background of patients may also play an important role in predicting the risk of readmission in heart failure patients. We developed methods for predicting the risk of rehospitalization in heart failure patients using models that integrate clinical characteristics with patient-reported functional limitations, behavioral and socio-economic characteristics. Our goal was to estimate the predictive accuracy of the joint model and compare it with models that make use of clinical data alone or behavioral and socio-economic characteristics alone, using real patient data. We collected data about the occurrence of hospital readmissions from a cohort of 789 heart failure patients for whom a range of clinical and behavioral characteristics data is also available. We applied the Cox model, four different variants of the Cox proportional hazards framework as well as an alternative non-parametric approach and determined the predictive accuracy for different categories of variables. The concordance index obtained from the joint prediction model including all types of variables was significantly higher than the accuracy obtained from using only clinical factors or using only behavioral, socioeconomic background and functional limitations in patients as predictors. Collecting information on behavior, patient-reported estimates of physical limitations and frailty and socio-economic data has significant value in the predicting the risk of readmissions with regards to heart failure events and can lead to substantially more accurate events prediction models

Asymptotics for Sparse Exponential Random Graph Models

We study the asymptotics for sparse exponential random graph models where the parameters may depend on the number of vertices of the graph. We obtain exact estimates for the mean and variance of the limiting probability distribution and the limiting log partition function of the edge-(single)-star model. They are in sharp contrast to the corresponding asymptotics in dense exponential random graph models. Similar analysis is done for directed sparse exponential random graph models parametrized by edges and multiple outward stars.Comment: 20 page

An empirical evaluation of hierarchical feature selection methods for classification in Bioinformatics datasets with gene ontology-based features

Hierarchical feature selection is a new research area in machine learning/data mining, which consists of performing feature selection by exploiting dependency relationships among hierarchically structured features. This paper evaluates four hierarchical feature selection methods, i.e., HIP, MR, SHSEL and GTD, used together with four types of lazy learning-based classifiers, i.e., Naïve Bayes, Tree Augmented Naïve Bayes, Bayesian Network Augmented Naïve Bayes and k-Nearest Neighbors classifiers. These four hierarchical feature selection methods are compared with each other and with a well-known “flat” feature selection method, i.e., Correlation-based Feature Selection. The adopted bioinformatics datasets consist of aging-related genes used as instances and Gene Ontology terms used as hierarchical features. The experimental results reveal that the HIP (Select Hierarchical Information Preserving Features) method performs best overall, in terms of predictive accuracy and robustness when coping with data where the instances’ classes have a substantially imbalanced distribution. This paper also reports a list of the Gene Ontology terms that were most often selected by the HIP method