Multivariate time series classification with temporal abstractions

The increase in the number of complex temporal datasets collected today has prompted the development of methods that extend classical machine learning and data mining methods to time-series data. This work focuses on methods for multivariate time-series classification. Time series classification is a challenging problem mostly because the number of temporal features that describe the data and are potentially useful for classification is enormous. We study and develop a temporal abstraction framework for generating multivariate time series features suitable for classification tasks. We propose the STF-Mine algorithm that automatically mines discriminative temporal abstraction patterns from the time series data and uses them to learn a classification model. Our experimental evaluations, carried out on both synthetic and real world medical data, demonstrate the benefit of our approach in learning accurate classifiers for time-series datasets. Copyright © 2009, Assocation for the Advancement of ArtdicaI Intelligence (www.aaai.org). All rights reserved

Event Discovery and Classification in Space-Time Series: A Case Study for Storms

Recent advancement in sensor technology has enabled the deployment of wireless sensors for surveillance and monitoring of phenomenon in diverse domains such as environment and health. Data generated by these sensors are typically high-dimensional and therefore difficult to analyze and comprehend. Additionally, high level phenomenon that humans commonly recognize, such as storms, fire, traffic jams are often complex and multivariate which individual univariate sensors are incapable of detecting. This thesis describes the Event Oriented approach, which addresses these challenges by providing a way to reduce dimensionality of space-time series and a way to integrate multivariate data over space and/or time for the purpose of detecting and exploring high level events. The proposed Event Oriented approach is implemented using space-time series data from the Gulf of Maine Ocean Observation System (GOMOOS). GOMOOS is a long standing network of wireless sensors in the Gulf of Maine monitoring the high energy ocean environment. As a case study, high level storm events are detected and classified using the Event Oriented approach. A domain-independent ontology for detecting high level xvi composite events called a General Composite Event Ontology is presented and used as a basis of the Storm Event Ontology. Primitive events are detected from univariate sensors and assembled into Composite Storm Events using the Storm Event Ontology. To evaluate the effectiveness of the Event Oriented approach, the resulting candidate storm events are compared with an independent historic Storm Events Database from the National Climatic Data Center (NCDC) indicating that the Event Oriented approach detected about 92% of the storms recorded by the NCDC. The Event Oriented approach facilitates classification of high level composite event. In the case study, candidate storms were classified based on their spatial progression and profile. Since ontological knowledge is used for constructing high level event ontology, detection of candidate high level events could help refine existing ontological knowledge about them. In summary, this thesis demonstrates the Event Oriented approach to reduce dimensionality in complex space-time series sensor data and the facility to integrate ime series data over space for detecting high level phenomenon

Early prediction of critical events in infants with single ventricle physiology in critical care using routinely collected data

Intensive care units (ICUs) provide care for critically-ill patients who require constant monitoring and the availability of specialized equipment and personnel. In this environment, a high volume of information and a high degree of uncertainty present a burden to clinicians. In specialized cohorts, such as pediatric patients with congenital heart defects (CHDs), this burden is exacerbated by increased complexity, the inadequacy of existing decision support aids, and the limited and decreasing availability of highly-specialized clinicians. Among CHD patients, infants with single ventricle (SV) physiology are one of the most complex and severely-ill sub-populations. While SV mortality rates have dropped, patient deterioration may happen unexpectedly in the period before patients undergo stage-2 palliative surgery. Even in expert hands, critical and potentially catastrophic events (CEs), such as cardiopulmonary resuscitation (CPR), emergent endotracheal intubation (EEI), or extracorporeal membrane oxygenation (ECMO) are common in SV patients, and may negatively impact morbidity, mortality, and hospital length of stay. There is a clinical need of predictive tools that help intensivists assess and forecast the advent of CEs in SV infants. Although ubiquitous, widely adopted ICU severity-of-illness scores or early warning systems (EWS), e.g., PRISM and PIM, have not met this need. They are often developed for general ICU use and do not generalize well to specialized populations. Furthermore, most EWS are developed for prediction of patient mortality. Among SV patients, however, death is semi-elective. On the other hand, prediction of CEs may help clinicians improve patient care by anticipating the advent of patient deterioration. In this dissertation, we aimed to develop and validate predictive models that achieve early and accurate prediction of CEs in infants with SV physiology. Such models may provide early and actionable information to clinicians and may be used to perform clinical interventions aimed at preventing CEs, and to reducing morbidity, mortality, and healthcare costs. We assert that our work is significant in that it addresses an unmet clinical need by achieving state-of-the-art, early prediction of patient deterioration in a challenging and vulnerable population

Multivariate Time Series Classification with Temporal Abstractions

Abstract The increase in the number of complex temporal datasets collected today has prompted the development of methods that extend classical machine learning and data mining methods to time-series data. This work focuses on methods for multivariate time-series classification. Time series classification is a challenging problem mostly because the number of temporal features that describe the data and are potentially useful for classification is enormous. We study and develop a temporal abstraction framework for generating multivariate time series features suitable for classification tasks. We propose the STF-Mine algorithm that automatically mines discriminative temporal abstraction patterns from the time series data and uses them to learn a classification model. Our experimental evaluations, carried out on both synthetic and real world medical data, demonstrate the benefit of our approach in learning accurate classifiers for time-series datasets