Conditional Anomaly Detection with Soft Harmonic Functions

International audienceIn this paper, we consider the problem of conditional anomaly detection that aims to identify data instances with an unusual response or a class label. We develop a new non-parametric approach for conditional anomaly detection based on the soft harmonic solution, with which we estimate the confidence of the label to detect anomalous mislabeling. We further regularize the solution to avoid the detection of isolated examples and examples on the boundary of the distribution support. We demonstrate the efficacy of the proposed method on several synthetic and UCI ML datasets in detecting unusual labels when compared to several baseline approaches. We also evaluate the performance of our method on a real-world electronic health record dataset where we seek to identify unusual patient-management decisions

Big data analytics for preventive medicine

© 2019, Springer-Verlag London Ltd., part of Springer Nature. Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations

Conditional Anomaly Detection Using Soft Harmonic Functions: An Application to Clinical Alerting

International audienceTimely detection of concerning events is an important problem in clinical practice. In this paper, we consider the problem of conditional anomaly detection that aims to identify data instances with an unusual response, such as the omission of an important lab test. We develop a new non-parametric approach for conditional anomaly detection based on the soft harmonic solution, with which we estimate the confidence of the label to detect anomalous mislabeling. We further regularize the solution to avoid the detection of isolated examples and examples on the boundary of the distribution support. We demonstrate the efficacy of the proposed method in detecting unusual labels on a real-world electronic health record dataset and compare it to several baseline approaches

Methods for event time series prediction and anomaly detection

Event time series are sequences of events occurring in continuous time. They arise in many real-world problems and may represent, for example, posts in social media, administrations of medications to patients, or adverse events, such as episodes of atrial fibrillation or earthquakes. In this work, we study and develop methods for prediction and anomaly detection on event time series. We study two general approaches. The first approach converts event time series to regular time series of counts via time discretization. We develop methods relying on (a) nonparametric time series decomposition and (b) dynamic linear models for regular time series. The second approach models the events in continuous time directly. We develop methods relying on point processes. For prediction, we develop a new model based on point processes to combine the advantages of existing models. It is flexible enough to capture complex dependency structures between events, while not sacrificing applicability in common scenarios. For anomaly detection, we develop methods that can detect new types of anomalies in continuous time and that show advantages compared to time discretization

Adaptive Graph-Based Algorithms for Conditional Anomaly Detection and Semi-Supervised Learning

We develop graph-based methods for conditional anomaly detection and semi-supervised learning based on label propagation on a data similarity graph. When data is abundant or arrive in a stream, the problems of computation and data storage arise for any graph-based method. We propose a fast approximate online algorithm that solves for the harmonic solution on an approximate graph. We show, both empirically and theoretically, that good behavior can be achieved by collapsing nearby points into a set of local representative points that minimize distortion. Moreover, we regularize the harmonic solution to achieve better stability properties. Anomaly detection techniques are used to identify anomalous (unusual) patterns in data. In clinical settings, these may concern identifications of unusual patient--state outcomes or unusual patient-management decisions. Therefore, we also present graph-based methods for detecting conditional anomalies and apply it to the identification of unusual clinical actions in hospitals. Our hypothesis is that patient-management actions that are unusual with respect to the past patients may be due to errors and that it is worthwhile to raise an alert if such a condition is encountered. Conditional anomaly detection extends standard unconditional anomaly framework but also faces new problems known as fringe and isolated points. We devise novel nonparametric graph-based methods to tackle these problems. Our methods rely on graph connectivity analysis and soft harmonic solution. Finally, we conduct an extensive human evaluation study of our conditional anomaly methods by 15 experts in critical care

Outlier Detection Techniques For Wireless Sensor Networks: A Survey

In the field of wireless sensor networks, measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the multivariate nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a decision tree to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier degree

Pattern Recognition in Intensive Care Online Monitoring

Clinical information systems can record numerous variables describing the patient’s state at high sampling frequencies. Intelligent alarm systems and suitable bedsidedecision support are needed to cope with this flood of information. A basic task here is the fast and correct detection of important patterns of change such as level shifts and trends in the data. We present approaches for automated pattern detection in online-monitoring data. Several methods based on curve fitting and statistical time series analysis are described. Median filtering can be used as a preliminary step to reduce the noise and to remove clinically irrelevant short term fluctuations. Our special focus is the potential of these methods for online-monitoring in intensive care. The strengths and weaknesses of the methods are discussed in this special context. The best approach may well be a suitable combination of the methods for achieving reliable results. Further investigations are needed to further improve the methods and their performance should be compared extensively in simulation studies and applications to real data