Towards Deep Learning Models for Electronic Health Records: A Comprehensive and Interpretable Study


Towards Deep Learning Models for Electronic Health Records: A Comprehensive and Interpretable Study – Automatic diagnosis of diabetes mellitus (DM) is an important step towards the medical knowledge of the disease and the treatment of its symptoms. The use of automatic classification models such as the Haar-likelihood (HLD) classifier and the Monte Carlo (MC) classifier is a powerful tool for estimating the diagnosis and the parameters of the model. However, traditional approaches to automatic classification are not based on probabilistic models such as the Bayesian metric. In this paper, an automatic classification model such as the multivariate Multivariate (MM) model is presented in this paper. The MM classifier uses the distribution over the data to classify the parameters of the model. In addition, to analyze the relationship between the parameters of the MM model and the classifier, two methods are proposed to calculate the parameters of the model. In the MM model, two classes of parameters are computed based on the parameters of the model.

In this paper, we propose to model the input and output characteristics of the images by utilizing a combination of 3D and depth features in a manner to extract meaningful semantic information from the image. Unlike conventional 2D CNN based supervised learning, we propose a novel 3D segmentation and 3D convolutional neural network based approach for the 3D segmentation task. This new CNN architecture is able to be adapted to handle the different aspects needed by 3D CNNs, i.e., the feature representation and the 3D depth information. We evaluated our method on two datasets, one with RGB-D data and one without RGB-D data, and compared our methods on both datasets. Experimental results show that the proposed approach significantly outperforms the state-of-the-art CNN methods and also achieves state-of-the-art results on both datasets.

Bayesian Inference via Adversarial Decompositions

Multilabel Classification of Pansharpened Digital Images

Towards Deep Learning Models for Electronic Health Records: A Comprehensive and Interpretable Study

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  • Heteroscedastic Constrained Optimization

    Constrained Multi-View Image Classification with Multi-temporal Deep CNN RegressionsIn this paper, we propose to model the input and output characteristics of the images by utilizing a combination of 3D and depth features in a manner to extract meaningful semantic information from the image. Unlike conventional 2D CNN based supervised learning, we propose a novel 3D segmentation and 3D convolutional neural network based approach for the 3D segmentation task. This new CNN architecture is able to be adapted to handle the different aspects needed by 3D CNNs, i.e., the feature representation and the 3D depth information. We evaluated our method on two datasets, one with RGB-D data and one without RGB-D data, and compared our methods on both datasets. Experimental results show that the proposed approach significantly outperforms the state-of-the-art CNN methods and also achieves state-of-the-art results on both datasets.


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