Deep Multitask Learning for Modeling Clinical Notes


Deep Multitask Learning for Modeling Clinical Notes – The paper presents a method to train large-scale convolutional neural network (CNN) classifiers. The paper shows that it is possible to extract the relevant features, a critical step for classifying handwritten words. The approach is based on a modified version of the deep learning technique Deep-Sparse Networks. A large number of samples are collected every time, a method based on CNNs is proposed. The experiments show that the proposed method can improve the classification accuracy on an average of 78.9% of the samples that are collected by CNN classifier.

Recently, many methods have been proposed to improve the precision of the semantic segmentation task. In this paper, two approaches are proposed to reduce the computational cost in semantic segmentation. First, a fast LSTM (Log2vec) classifier is employed by the algorithm that uses LSTMs as the input. A deep learning algorithm is used to train this classifier. In addition, a distance measure is devised to measure the precision. For all tested algorithms, the proposed method achieves a 95.99% accuracy on semantic segmentation task.

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Deep Multitask Learning for Modeling Clinical Notes

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    Fast Low-Rank Matrix Estimation for High-Dimensional Text ClassificationRecently, many methods have been proposed to improve the precision of the semantic segmentation task. In this paper, two approaches are proposed to reduce the computational cost in semantic segmentation. First, a fast LSTM (Log2vec) classifier is employed by the algorithm that uses LSTMs as the input. A deep learning algorithm is used to train this classifier. In addition, a distance measure is devised to measure the precision. For all tested algorithms, the proposed method achieves a 95.99% accuracy on semantic segmentation task.


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