Classification-driven temporal discretization of multivariate time series

Moskovitch, Robert; Shaḥar, Yuval

doi:10.1007/s10618-014-0380-z

Cited by 89 publications

(50 citation statements)

References 37 publications

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“…These models are listed in Table 3, along with their references. Note that, though Random Forest (RF) was originally designed for classification problems, here, the range of the class variable is discretized so that such an algorithm can be adopted for regression problems [93]. …”

Section: Methodsmentioning

confidence: 99%

How are you feeling?: A personalized methodology for predicting mental states from temporally observable physical and behavioral information

Tuarob

Tucker

Kumara

et al. 2017

Journal of Biomedical Informatics

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It is believed that anomalous mental states such as stress and anxiety not only cause suffering for the individuals, but also lead to tragedies in some extreme cases. The ability to predict the mental state of an individual at both current and future time periods could prove critical to healthcare practitioners. Currently, the practical way to predict an individual’s mental state is through mental examinations that involve psychological experts performing the evaluations. However, such methods can be time and resource consuming, mitigating their broad applicability to a wide population. Furthermore, some individuals may also be unaware of their mental states or may feel uncomfortable to express themselves during the evaluations. Hence, their anomalous mental states could remain undetected for a prolonged period of time. The objective of this work is to demonstrate the ability of using advanced machine learning based approaches to generate mathematical models that predict current and future mental states of an individual. The problem of mental state prediction is transformed into the time series forecasting problem, where an individual is represented as a multivariate time series stream of monitored physical and behavioral attributes. A personalized mathematical model is then automatically generated to capture the dependencies among these attributes, which is used for prediction of mental states for each individual. In particular, we first illustrate the drawbacks of traditional multivariate time series forecasting methodologies such as vector autoregression. Then, we show that such issues could be mitigated by using machine learning regression techniques which are modified for capturing temporal dependencies in time series data. A case study using the data from 150 human participants illustrates that the proposed machine learning based forecasting methods are more suitable for high-dimensional psychological data than the traditional vector autoregressive model in terms of both magnitude of error and directional accuracy. These results not only present a successful usage of machine learning techniques in psychological studies, but also serve as a building block for multiple medical applications that could rely on an automated system to gauge individuals’ mental states.

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Section: Methodsmentioning

confidence: 99%

How are you feeling?: A personalized methodology for predicting mental states from temporally observable physical and behavioral information

Tuarob

Tucker

Kumara

et al. 2017

Journal of Biomedical Informatics

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“…There are also algorithms that transform time series into simpler representations while aiming at preserving some of the underlying temporal order. These algorithms include Discrete Fourier Transformation (DFT) [17], Discrete Wavelet Transformation (DWT) [18], Symbolic Aggregate approXimation (SAX) [19,20], Equal Width Discretization (EWD), Equal Frequency Discretization (EFD), Persist [21] and Temporal Discretization for Classication (TD4C) [22]. The SAX representation is often preferred since it is intuitive and easy to comprehend while maintaining competitive performance against other representations.…”

Section: Introductionmentioning

confidence: 99%

“…The SAX representation is often preferred since it is intuitive and easy to comprehend while maintaining competitive performance against other representations. Although the TD4C algorithm has been shown to outperform the SAX representation for the task of classication of multivariate temporal data [22], it is a supervised learning method hence requires the availability of class labels. Regarding the lack of reference points for comparing similarities between time series, the use of shapelets, i.e., time series subsequences, have been proposed [23,24,25].…”

Section: Introductionmentioning

confidence: 99%

Learning from heterogeneous temporal data in electronic health records

Zhao

Papapetrou

Asker

et al. 2017

Journal of Biomedical Informatics

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Electronic health records contain large amounts of longitudinal data that are valuable for biomedical informatics research. The application of machine learning is a promising alternative to manual analysis of such data. However, the complex structure of the data, which includes clinical events that are unevenly distributed over time, poses a challenge for standard learning algorithms. Some approaches to modeling temporal data rely on extracting single values from time series; however, this leads to the loss of potentially valuable sequential information. How to better account for the temporality of clinical data, hence, remains an important research question. In this study, novel representations of temporal data in electronic health records are explored. These representations retain the sequential information, and are directly compatible with standard machine learning algorithms. The explored methods are based on symbolic sequence representations of time series data, which are utilized in a number of different ways. An empirical investigation, using 19 datasets comprising clinical measurements observed over time from a real database of electronic health records, shows that using a distance measure to random subsequences leads to substantial improvements in predictive performance compared to using the original sequences or clustering the sequences. Evidence is moreover provided on the quality of the symbolic sequence representation by comparing it to sequences that are generated using domain knowledge by clinical experts. The proposed method creates representations that better account for the temporality of clinical events, which is often key to prediction tasks in the biomedical domain.

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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%