to evaluate off-label and off-nccn guidelines uses of antineoplastic drugs in a major chinese hospital.totally 1122 patients were selected from july to december 2011. then, the off-label and off-nccn guidelines uses of antineoplastic drugs were analyzed.in 798 of 1122 patients (71.12%), drugs were used for off-label. in 317 of 1122 patients (28.25%), the drugs were prescribed for off-label and ...

Automatic feature learning algorithms are at the forefront of modern day machine learning research. We present a novel algorithm, supervised Q-walk, which applies Q-learning to generate random walks on graphs such that the walks prove to be useful for learning node features suitable for tackling with the node classification problem. We present another novel algorithm, k-hops neighborhood based ...

We consider the statistical problem of learning common source of variability in data which are synchronously captured by multiple sensors, and demonstrate that Siamese neural networks can be naturally applied to this problem. This approach is useful in particular in exploratory, data-driven applications, where neither a model nor label information is available. In recent years, many researchers...

Data cube is a cornerstone architecture in multidimensional analysis of structured datasets. It is highly desirable to conduct multidimensional analysis on text corpora with cube structures for various text-intensive applications in healthcare, business intelligence, and social media analysis. However, one bottleneck to constructing text cube is to automatically put millions of documents into t...

The semi-supervised learning usually only predict labels for unlabeled data appearing in training data, and cannot effectively predict labels for testing data never appearing in training set. To handle this outof-sample problem, many inductive methods make a constraint such that the predicted label matrix should be exactly equal to a linear model. In practice, this constraint is too rigid to ca...

An appearance-based face recognition approach called the L-Fisherfaces is proposed in this paper, By using Local Fisher Discriminant Embedding (LFDE), the face images are mapped into a face subspace for analysis. Different from Linear Discriminant Analysis (LDA), which effectively sees only the Euclidean structure of face space, LFDE finds an embedding that preserves local information, and obta...

This work addresses the task of multilabel image classification. Inspired by the great success from deep convolutional neural networks (CNNs) for single-label visualsemantic embedding, we exploit extending these models for multilabel images. Specifically, we propose an imagedependent ranking model, which returns a ranked list of labels according to its relevance to the input image. In contrast ...

Goal–oriented (GO) dialogue systems rely on an initial natural language understanding (NLU) module to determine the user’s intention and parameters thereof — also known as slots. Since the systems, also known as bots, help the users with solving problems in relatively narrow domains, they require training data within those domains. This leads to significant data availability issues that inhibit...

Effective visualization is vital for understanding a complex network, in particular its dynamical aspect such as information diffusion process. Existing node embedding methods are all based solely on the network topology and sometimes produce counter-intuitive visualization. A new node embedding method based on conditional probability is proposed that explicitly addresses diffusion process usin...

Sparse subspace learning has drawn more and more attentions recently. However, most of the sparse subspace learning methods are unsupervised and unsuitable for classification tasks. In this paper, a new sparse subspace learning algorithm called discriminant sparse neighborhood preserving embedding (DSNPE) is proposed by adding the discriminant information into sparse neighborhood preserving emb...