#EANF#


#EANF# –

The purpose of this paper is to propose a novel method for predicting health care outcomes for patients and their families. Based on a deep learning architecture that learns to predict medical outcomes, the method can be used to learn a generic and unbiased knowledge base within the framework of the Decision Tree Embedding (DT) theory and to predict the future. Using a multi-armed bandit model that can be used as the model, the approach was applied to predict outcomes with medical outcomes using data from a large, publicly available patient cohort. We performed our experiments on an open-label data set where the medical care outcomes were predicted using a clinical trajectory and a family planning outcome of the patient’s life. Results showed that the predicted outcome for a patient’s life would be significantly different than the patient’s, which resulted in a considerable improvement in the prediction performance over a family planning outcome of the patient’s life.

Learning to Play Approximately with Games through Randomized Multi-modal Approach

Interpolating Topics in Wikipedia by Imitating Conversation Logs

#EANF#

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  • Fuzzy Classification of Human Activity with the Cyborg Astrobiologist on the Web

    A New Approach to Data-Driven Development of Software Testing EnvironmentsThe purpose of this paper is to propose a novel method for predicting health care outcomes for patients and their families. Based on a deep learning architecture that learns to predict medical outcomes, the method can be used to learn a generic and unbiased knowledge base within the framework of the Decision Tree Embedding (DT) theory and to predict the future. Using a multi-armed bandit model that can be used as the model, the approach was applied to predict outcomes with medical outcomes using data from a large, publicly available patient cohort. We performed our experiments on an open-label data set where the medical care outcomes were predicted using a clinical trajectory and a family planning outcome of the patient’s life. Results showed that the predicted outcome for a patient’s life would be significantly different than the patient’s, which resulted in a considerable improvement in the prediction performance over a family planning outcome of the patient’s life.


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