Classification of multivariate time series via temporal abstraction and time intervals mining

Moskovitch, Robert; Shaḥar, Yuval

doi:10.1007/s10115-014-0784-5

Cited by 86 publications

(60 citation statements)

References 27 publications

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“…The increased attention to the subject of mining time intervals has led several research groups to quite simultaneously propose using the discovered temporal patterns as features for classifying multivariate time series (Patel et al 2008;Batal et al 2012Batal et al , 2013, including the suggestion of a highly preliminary version of the KarmaLegoS framework Moskovitch and Shahar 2014). Interestingly, all of the studies that reported the use of temporal abstraction and time intervals mining for the purpose of classification were using datasets from the biomedical domain (Patel et al 2008;Batal et al 2012Batal et al , 2013.…”

Section: Classification Via Frequent Patternsmentioning

confidence: 98%

Classification-driven temporal discretization of multivariate time series

Moskovitch

Shaḥar

2014

Data Min Knowl Disc

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Biomedical data, in particular electronic medical records data, include a large number of variables sampled in irregular fashion, often including both time point and time intervals, thus providing several challenges for analysis and data mining. Classification of multivariate time series data is a challenging task, but is often necessary for medical care or research. Increasingly, temporal abstraction, in which a series of raw-data time points is abstracted into a set of symbolic time intervals, is being used for classification of multivariate time series. In this paper, we introduce a novel supervised discretization method, geared towards enhancement of classification accuracy, which determines the cutoffs that will best discriminate among classes through the distribution of their states. We present a framework for classification of multivariate time series analysis, which implements three phases: (1) application of a temporalabstraction process that transforms a series of raw time-stamped data points into a series of symbolic time intervals (based on either unsupervised or supervised temporal abstraction); (2) mining these time intervals to discover frequent temporal-interval relation patterns (TIRPs), using versions of Allen's 13 temporal relations; (3) using the patterns as features to induce a classifier. We evaluated the framework, focusing on the comparison of three versions of the new, supervised, temporal discretization for Responsible editors: classification (TD4C) method, each relying on a different symbolic-state distributiondistance measure among outcome classes, to several commonly used unsupervised methods, on real datasets in the domains of diabetes, intensive care, and infectious hepatitis. Using only three abstract temporal relations resulted in a better classification performance than using Allen's seven relations, especially when using three symbolic states per variable. Similarly when using the horizontal support and mean duration as the TIRPs feature representation, rather than a binary (existence) representation. The classification performance when using the three versions of TD4C was superior to the performance when using the unsupervised (EWD, SAX, and KB) discretization methods.

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Section: Classification Via Frequent Patternsmentioning

confidence: 98%

Classification-driven temporal discretization of multivariate time series

Moskovitch

Shaḥar

2014

Data Min Knowl Disc

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“…However, it is also possible to abstract point-based data by applying temporal knowledge which results in a more abstract representation of the data, in the form of symbolic time intervals Batal et al provide several pattern mining techniques that uses a time interval-related representation of a sequence, which requires either the events have continuous values that can be quantized or the duration of every event is available [36,42]. Moskovitch et al provide several approaches for discretizing continuous event values to derive more discriminative time-interval related patterns [40,41]. Patel et al also provide a technique for mining interval-based events [43].…”

Section: Background and Significancementioning

confidence: 99%

Mining and exploring care pathways from electronic medical records with visual analytics

Perer

Wang

2015

Journal of Biomedical Informatics

100

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“…Some of these intervalbased methods have been specifically extended to handle classification and prediction tasks, in particular, in clinical domains [38].…”

Section: Figurementioning

confidence: 99%

Sequence Mining of Comorbid Neurodevelopmental Disorders Using the SPADE Algorithm

Pimus¹,

Peleg

Schertz³

2016

Methods Inf Med

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SummaryObjectives: Understanding the progression of comorbid neurodevelopmental disorders (NDD) during different critical time periods may contribute to our comprehension of the underlying pathophysiology of NDDs. The objective of our study was to identify frequent temporal sequences of developmental diagnoses in noisy patient data. Methods: We used a data set of 2810 patients, documenting NDD diagnoses given to them by an NDD expert at a child developmental center during multiple visits at different ages. Extensive preprocessing steps were developed in order to allow the data set to be processed by an efficient sequence mining algorithm (SPADE). Results: The discovered sequences were validated by cross validation for 10 iterations; all correlation coefficients for support, confidence and lift measures were above 0.75 and their proportions were similar. No significant differences between the distributions of sequences were found using KolmogorovSmirnov test. Conclusions:We have demonstrated the feasibility of using the SPADE algorithm for discovery of valid temporal sequences of comorbid disorders in children with NDDs. The identification of such sequences would be beneficial from clinical and research perspectives. Moreover, these sequences could serve as features for developing a full-fledged temporal predictive model.

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Classification of multivariate time series via temporal abstraction and time intervals mining

Cited by 86 publications

References 27 publications

Classification-driven temporal discretization of multivariate time series

Classification-driven temporal discretization of multivariate time series

Mining and exploring care pathways from electronic medical records with visual analytics

Sequence Mining of Comorbid Neurodevelopmental Disorders Using the SPADE Algorithm

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