3,125 research outputs found
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
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
Statistical Mechanics and Information-Theoretic Perspectives on Complexity in the Earth System
Peer reviewedPublisher PD
Deep generative modelling of the imaged human brain
Human-machine symbiosis is a very promising opportunity for the field of neurology given that the interpretation of the imaged human brain is a trivial feat
for neither entity. However, before machine learning systems can be used in
real world clinical situations, many issues with automated analysis must first be
solved. In this thesis I aim to address what I consider the three biggest hurdles
to the adoption of automated machine learning interpretative systems. For each
issue, I will first elucidate the reader on its importance given the overarching
narratives of both neurology and machine learning, and then showcase my proposed solutions to these issues through the use of deep generative models of the
imaged human brain.
First, I start by addressing what is an uncontroversial and universal sign of intelligence: the ability to extrapolate knowledge to unseen cases. Human neuroradiologists have studied the anatomy of the healthy brain and can therefore,
with some success, identify most pathologies present on an imaged brain, even
without having ever been previously exposed to them. Current discriminative
machine learning systems require vast amounts of labelled data in order to accurately identify diseases. In this first part I provide a generative framework that
permits machine learning models to more efficiently leverage unlabelled data for
better diagnoses with either none or small amounts of labels.
Secondly, I address a major ethical concern in medicine: equitable evaluation
of all patients, regardless of demographics or other identifying characteristics.
This is, unfortunately, something that even human practitioners fail at, making
the matter ever more pressing: unaddressed biases in data will become biases
in the models. To address this concern I suggest a framework through which
a generative model synthesises demographically counterfactual brain imaging
to successfully reduce the proliferation of demographic biases in discriminative
models.
Finally, I tackle the challenge of spatial anatomical inference, a task at the centre
of the field of lesion-deficit mapping, which given brain lesions and associated
cognitive deficits attempts to discover the true functional anatomy of the brain.
I provide a new Bayesian generative framework and implementation that allows
for greatly improved results on this challenge, hopefully, paving part of the road
towards a greater and more complete understanding of the human brain
Explainable Contextual Anomaly Detection using Quantile Regression Forests
Traditional anomaly detection methods aim to identify objects that deviate
from most other objects by treating all features equally. In contrast,
contextual anomaly detection methods aim to detect objects that deviate from
other objects within a context of similar objects by dividing the features into
contextual features and behavioral features. In this paper, we develop
connections between dependency-based traditional anomaly detection methods and
contextual anomaly detection methods. Based on resulting insights, we propose a
novel approach to inherently interpretable contextual anomaly detection that
uses Quantile Regression Forests to model dependencies between features.
Extensive experiments on various synthetic and real-world datasets demonstrate
that our method outperforms state-of-the-art anomaly detection methods in
identifying contextual anomalies in terms of accuracy and interpretability.Comment: Manuscript submitted to Data Mining and Knowledge Discovery in
October 2022 for possible publication. This is the revised version submitted
in April 202
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