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One of the most formidable challenges electronic health records (EHRs) pose for traditional analytics is the inability to map directly (or reliably) to medical concepts or phenotypes. Among other things, EHR-based phenotyping can help identify and target patients for interventions and improve real-time clinical decisions. Existing phenotyping approaches often require labor-intensive supervision from...
Factorization Machine (FM) provides a generic framework that combines the prediction quality of factorization models with the flexibility of feature engineering that discriminative models like SVM offer. The Bayesian Factorization Machine [11], with its impressive predictive performance and the convenience of automatic tuning of parameters, has been one of the most successful and efficient approaches...
With vast amount of medical records being digitized in recent years in the form of Electronic Health Records (EHRs), accurate and large scale automated prognosis of diseases has become a possibility. However, most existing works in disease prediction have focused on a single condition or a few related conditions. Such models do not account for the fact that multiple conditions may co-occur in patients...
We introduce a class of methods for Gaussian process regression with functional expectation constraints. We show that the solution can be found without the need for approximations when the constraint set satisfies a representation theorem. Further, the solution is unique when the constraint set is convex. Constrained Gaussian process regression is motivated by the modeling of transposable (matrix)...
ICU patients are vulnerable to in-ICU morbidities and mortality, making accurate systems for identifying at-risk patients a necessity for improving clinical care. Here, we present an improved model for predicting in-hospital mortality using data collected from the first 48 hours of a patient's ICU stay. We generated predictive features for each patient using demographic data, the number of observations...
This paper introduces a privacy-aware Bayesian approach that combines ensembles of classifiers and clusterers to perform semi-supervised and transductive learning. We consider scenarios where instances and their classification/clustering results are distributed across different data sites and have sharing restrictions. As a special case, the privacy aware computation of the model when instances of...
This paper proposes a novel framework called Gaussian process maximum likelihood for spatially adaptive classification of hyperspectral data. In hyperspectral images, spectral responses of land covers vary over space, and conventional classification algorithms that result in spatially invariant solutions are fundamentally limited. In the proposed framework, each band of a given class is modeled by...
Several data mining applications such as recommender systems and online advertising involve the analysis of large, heterogeneous dyadic data, where the data consists of measurements on pairs of elements, each from a different set of entities. Independent variables (covariates) are additionally associated with the entities along the two modes and their combination. This paper focuses on developing...
Many data mining applications involve predictive modeling of very large, complex datasets. Such applications present a need for innovative algorithms and associated implementations that are not only effective in terms of prediction accuracy, but can also be efficiently run on distributed computational systems to yield results in reasonable time. This paper focuses on predictive modeling of multirelational...
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