Description:
There has been a continuing demand for traditional and complementary medicine worldwide. A fundamental and important topic in Traditional Chinese Medicine (TCM) is to optimize the prescription and to detect herb regularities from TCM data. In this paper, we propose a novel clustering model to solve this general problem of herb categorization, a pivotal task of prescription optimization and herb regularities. The model utilizes Random Walks method, Bayesian rules and Expectation Maximization(EM) models to complete a clustering analysis effectively on a heterogeneous information network. We performed extensive experiments on the real-world datasets and compared our method with other algorithms and experts. Experimental results have demonstrated the effectiveness of the proposed model for discovering useful categorization of herbs and its potential clinical manifestations.
Description:
Regularities analysis for prescriptions is a significant task for traditional Chinese medicine (TCM), both in inheritance of clinical experience and in improvement of clinical quality. Recently, many methods have been proposed for regularities discovery, but this task is challenging due to the quantity, sparsity and free-style of prescriptions. In this paper, we address the specific problem of regularities discovery and propose a graph embedding based framework for regularities discovery for massive prescriptions. We model this task as a relation prediction in which the correlation of two herbs or of herb and symptom are incorporated to characterize the different relationships. Specifically, we first establish a heterogeneous network with herbs and symptoms as its nodes. We develop a bipartite embedding model termed HS2Vec to detect regularities, which explores multiple relations of herbherb, and herb-symptom based on the heterogeneous network. Experiments on four real-world datasets demonstrate that the proposed framework is very effective for regularities discovery.
Description:
Uncertain data streams have been widely generated in many Web applications. The uncertainty in data streams makes anomaly detection from sensor data streams far more challenging. In this article, we present a novel framework that supports anomaly detection in uncertain data streams. The proposed framework adopts the wavelet soft-thresholding method to remove the noises or errors in data streams. Based on the refined data streams, we develop effective period pattern recognition and feature extraction techniques to improve the computational efficiency. We use classification methods for anomaly detection in the corrected data stream. We also empirically show that the proposed approach shows a high accuracy of anomaly detection on several real datasets.
Description:
Data stream mining has been studied in diverse application domains. In recent years, a population aging is stressing the national and international health care systems. Anomaly detection is a typical example of a data streams application. It is a dynamic process of finding abnormal behaviours from given data streams. In this paper, we discuss the existing anomaly detection techniques for Medical data streams. In addition, we present a process of using the Autoregressive Integrated Moving Average model (ARIMA) to analyse the ECG data streams.
Description:
Real-world time series classification applications often involve positive unlabeled (PU) training data, where there are only a small set PL of positive labeled examples and a large set U of unlabeled ones. Most existing time series PU classification methods utilize all readings in the time series, making them sensitive to non-characteristic readings. Characteristic patterns named shapelets present a promising solution to this problem, yet discovering shapelets under PU settings is not easy. In this paper, we take on the challenging task of shapelet discovery with PU data. We propose a novel pattern ensemble technique utilizing both characteristic and non-characteristic patterns to rank U examples by their possibilities of being positive. We also present a novel stopping criterion to estimate the number of positive examples in U. These enable us to effectively label all U training examples and conduct supervised shapelet discovery. The shapelets are then used to build a one-nearest-neighbor classifier for online classification. Extensive experiments demonstrate the effectiveness of our method.
Description:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Description:
Real-world time series classification applications often involve positive unlabeled (PU) training data, where there are only a small set PL of positive labeled examples and a large set U of unlabeled ones. Most existing time series PU classification methods utilize all readings in the time series, making them sensitive to non-characteristic readings. Characteristic patterns named shapelets present a promising solution to this problem, yet discovering shapelets under PU settings is not easy. In this paper, we take on the challenging task of shapelet discovery with PU data. We propose a novel pattern ensemble technique utilizing both characteristic and non-characteristic patterns to rank U examples by their possibilities of being positive. We also present a novel stopping criterion to estimate the number of positive examples in U. These enable us to effectively label all U training examples and conduct supervised shapelet discovery. The shapelets are then used to build a one-nearest-neighbor classifier for online classification. Extensive experiments demonstrate the effectiveness of our method.
Description:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Description:
Cardiac arrhythmia has been identified as a type of cardiovascular diseases (CVDs) that causes approximately 12%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$12\%$$\end{document} of all deaths globally. The current progress on arrhythmia detection based on ECG recordings is facing a bottleneck for adopting single classifier and static ensemble methods. Besides, most of the work tend to use a static feature set for characterizing all types of heartbeats, which may limit the classification performance. To fill in the gap, a novel framework called D-ECG is proposed to introduce dynamic ensemble selection (DES) technique to provide accurate detection of cardiac arrhythmia. In addition, the proposed D-ECG develops a result regulator that use different features to refine the classification result from the DES technique. The results reported in this paper have shown visible improvement on the overall heartbeat classification accuracy as well as the sensitivity of disease heartbeats.