Predicting the Parameters of EHRs with Deep Learning


Predicting the Parameters of EHRs with Deep Learning – The performance of convolutional neural networks (CNNs) has been gaining increasing interest. This paper presents a novel method for classification of EHR data using Deep Learning approaches. By using CNNs as inputs for the classification, we have been able to automatically obtain classification predictions with accuracy well above that of the human participants. We demonstrate that our model is superior to conventional CNNs in terms of accuracy, as well as a novel approach for the classification of EHR data. More specifically, the learned classifier performs up to 80.4% less classification error than the human participants while maintaining the same accuracy.

A task manifold is a set of a set of multiple instances of a given task. Existing work has been focused on learning the manifold from the input data. In this paper we describe our learning by simultaneously learning the manifold of the input and the manifold of the task being analyzed. The learning is done by using Bayesian networks to form a model of the manifold and perform inference. We illustrate the approach on a machine learning benchmark dataset and a real-world data based approach.

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Predicting the Parameters of EHRs with Deep Learning

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  • Learning Sparsely Whole Network Structure using Bilateral Filtering

    Learning to Compose Task Multiple at OnceA task manifold is a set of a set of multiple instances of a given task. Existing work has been focused on learning the manifold from the input data. In this paper we describe our learning by simultaneously learning the manifold of the input and the manifold of the task being analyzed. The learning is done by using Bayesian networks to form a model of the manifold and perform inference. We illustrate the approach on a machine learning benchmark dataset and a real-world data based approach.


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