Activity

As a combination of fuzzy sets and covering rough sets, fuzzy β covering has attracted much attention in recent years. The fuzzy β neighborhood serves as the basic granulation unit of fuzzy β covering. In this article, a new discernibility measure with respect to the fuzzy β neighborhood is proposed to characterize the distinguishing ability of a fuzzy covering family. To this end, the parameterized fuzzy β neighborhood is introduced to describe the similarity between samples, where the distinguishing ability of a given fuzzy covering family can be evaluated. Some variants of the discernibility measure, such as the joint discernibility measure, conditional discernibility measure, and mutual discernibility measure, are then presented to reflect the change of distinguishing ability caused by different fuzzy covering families. These measures have similar properties as the Shannon entropy. Finally, to deal with knowledge reduction with fuzzy β covering, we formalize a new type of decision table, that is, fuzzy β covering decision tables. The data reduction of fuzzy covering decision tables is addressed from the viewpoint of maintaining the distinguishing ability of a fuzzy covering family, and a forward attribute reduction algorithm is designed to reduce redundant fuzzy coverings. Extensive experiments show that the proposed method can effectively evaluate the uncertainty of different types of datasets and exhibit better performance in attribute reduction compared with some existing algorithms.Medical hyperspectral imagery has recently attracted considerable attention. However, for identification tasks, the high dimensionality of hyperspectral images usually leads to poor performance. Thus, dimensionality reduction (DR) is crucial in hyperspectral image analysis. Motivated by exploiting the underlying structure information of medical hyperspectral images and enhancing the discriminant ability of features, a discriminant tensor-based manifold embedding (DTME) is proposed for discriminant analysis of medical hyperspectral images. Based on the idea of manifold learning, a new discriminant similarity metric is designed, which takes into account the tensor representation, sparsity, low-rank and distribution characteristics. Then, an inter-class tensor graph and an intra-class tensor graph are constructed using the new similarity metric to reveal intrinsic manifold of hyperspectral data. Dimensionality reduction is achieved by embedding this supervised tensor graphs into the low-dimensional tensor subspace. Experimental results on membranous nephropathy and white bloodcells identification tasks demonstrate the potential clinical value of the proposed DTME.Understanding the individualized risks of undertaking surgical procedures is essential to personalize preparatory, intervention and post-care protocols for minimizing post-surgical complications. selleck products This knowledge is key in oncology given the nature of interventions, the fragile profile of patients with comorbidities and cytotoxic drug exposure, and the possible cancer recurrence. Despite its relevance, the discovery of discriminative patterns of post-surgical risk is hampered by major challenges i) the unique physiological and demographic profile of individuals, as well as their differentiated post-surgical care; ii) the high-dimensionality and heterogeneous nature of available biomedical data, combining non-identically distributed risk factors, clinical and molecular variables; iii) the need to generalize tumors have significant histopathological differences and individuals undertake unique surgical procedures; iv) the need to focus on non-trivial patterns of post-surgical risk, while guaranteeing their statisitation protocols and bedside care.Survival analysis is a commonly used method in the medical field to analyze and predict the time of events. In medicine, this approach plays a key role in determining the course of treatment, developing new drugs, and improving hospital procedures. Most of the existing work in this area has addressed the problem by making strong assumptions about the underlying stochastic process. However, these assumptions are usually violated in the real-world data. This paper proposed a semisupervised multitask learning (SSMTL) method based on deep learning for survival analysis with or without competing risks. SSMTL transforms the survival analysis problem into a multitask learning problem that includes semisupervised learning and multipoint survival probability prediction. The distribution of survival times and the relationship between covariates and outcomes were modeled directly without any assumptions. Semisupervised loss and ranking loss are used to deal with censored data and the prior knowledge of the nonincreasing trend of the survival probability. Additionally, the importance of prognostic factors is determined, and the time-dependent and nonlinear effects of these factors on survival outcomes are visualized. The prediction performance of SSMTL is better than that of previous models in settings with or without competing risks, and the effects of predictors are successfully described. This study is of great significance for the exploration and application of deep learning methods involving medical structured data and provides an effective deep-learning-based method for survival analysis with complex-structured clinical data.The diagnosis of obstructive sleep apnea is based on daytime symptoms and the frequency of respiratory events during the night. The respiratory events are scored manually from polysomnographic recordings, which is time-consuming and expensive. Therefore, automatic scoring methods could considerably improve the efficiency of sleep apnea diagnostics and release the resources currently needed for manual scoring to other areas of sleep medicine. In this study, we trained a long short-term memory neural network for automatic scoring of respiratory events using input signals from peripheral blood oxygen saturation, thermistor-airflow, nasal pressure -airflow, and thorax respiratory effort. The signals were extracted from 887 in-lab polysomnography recordings. 787 patients with suspected sleep apnea were used to train the neural network and 100 patients were used as an independent test set. The epoch-wise agreement between manual and automatic neural network scoring was high (88.9%, =0.728). In addition, the apnea-hypopnea index (AHI) calculated from the automated scoring was close to the manually determined AHI with a mean absolute error of 3.