Online mining abnormal period patterns from multiple medical sensor data streams

Huang, Guangyan; Zhang, Yanchun; Cao, Jie; Steyn, Michael; Taraporewalla, Kersi

doi:10.1007/s11280-013-0203-y

Cited by 20 publications

(17 citation statements)

References 18 publications

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“…Sharing this data with large commercial players who have the programming and processing ability to extract multiple signals from that data is even more difficult [27]. Only three papers included in our review addressed use of machine learning to identify data errors [28][29][30]. Errors are common in routine EHR data [6], and thus, datasets must be cleaned before analyses.…”

Section: Results In Context With Literaturementioning

confidence: 99%

Use of machine learning to analyse routinely collected intensive care unit data: a systematic review

et al. 2019

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Background Intensive care units (ICUs) face financial, bed management, and staffing constraints. Detailed data covering all aspects of patients’ journeys into and through intensive care are now collected and stored in electronic health records: machine learning has been used to analyse such data in order to provide decision support to clinicians. Methods Systematic review of the applications of machine learning to routinely collected ICU data. Web of Science and MEDLINE databases were searched to identify candidate articles: those on image processing were excluded. The study aim, the type of machine learning used, the size of dataset analysed, whether and how the model was validated, and measures of predictive accuracy were extracted. Results Of 2450 papers identified, 258 fulfilled eligibility criteria. The most common study aims were predicting complications (77 papers [29.8% of studies]), predicting mortality (70 [27.1%]), improving prognostic models (43 [16.7%]), and classifying sub-populations (29 [11.2%]). Median sample size was 488 (IQR 108–4099): 41 studies analysed data on > 10,000 patients. Analyses focused on 169 (65.5%) papers that used machine learning to predict complications, mortality, length of stay, or improvement of health. Predictions were validated in 161 (95.2%) of these studies: the area under the ROC curve (AUC) was reported by 97 (60.2%) but only 10 (6.2%) validated predictions using independent data. The median AUC was 0.83 in studies of 1000–10,000 patients, rising to 0.94 in studies of > 100,000 patients. The most common machine learning methods were neural networks (72 studies [42.6%]), support vector machines (40 [23.7%]), and classification/decision trees (34 [20.1%]). Since 2015 (125 studies [48.4%]), the most common methods were support vector machines (37 studies [29.6%]) and random forests (29 [23.2%]). Conclusions The rate of publication of studies using machine learning to analyse routinely collected ICU data is increasing rapidly. The sample sizes used in many published studies are too small to exploit the potential of these methods. Methodological and reporting guidelines are needed, particularly with regard to the choice of method and validation of predictions, to increase confidence in reported findings and aid in translating findings towards routine use in clinical practice. Electronic supplementary material The online version of this article (10.1186/s13054-019-2564-9) contains supplementary material, which is available to authorized users.

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Section: Results In Context With Literaturementioning

confidence: 99%

Use of machine learning to analyse routinely collected intensive care unit data: a systematic review

et al. 2019

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“…Clustering data streams is still an open problem with room for improvement [38]. Increasing the classification efficiency in this dynamic environment has a great potential in several application fields, from intrusion detection [39] to abnormality detection in patients' physiological data streams [40]. In this light, the proposed methodology draws its inspiration from key features of the successful methods listed in Section 2, with the final goal of improving upon the current state-of-the-art.…”

Section: Motivations Objectives and Methodsmentioning

confidence: 99%

A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation

et al. 2019

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This article presents the Optimised Stream clustering algorithm (OpStream), a novel approach to cluster dynamic data streams. The proposed system displays desirable features, such as a low number of parameters and good scalability capabilities to both high-dimensional data and numbers of clusters in the dataset, and it is based on a hybrid structure using deterministic clustering methods and stochastic optimisation approaches to optimally centre the clusters. Similar to other state-of-the-art methods available in the literature, it uses "microclusters" and other established techniques, such as density based clustering. Unlike other methods, it makes use of metaheuristic optimisation to maximise performances during the initialisation phase, which precedes the classic online phase. Experimental results show that OpStream outperforms the state-of-the-art methods in several cases, and it is always competitive against other comparison algorithms regardless of the chosen optimisation method. Three variants of OpStream, each coming with a different optimisation algorithm, are presented in this study. A thorough sensitive analysis is performed by using the best variant to point out OpStream's robustness to noise and resiliency to parameter changes.

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“…Based on Fig.6, we set = 1 and λ = 10 for inflexion detection and time series compression. We then compare three methods of period point representation: (1) inflexions in CT S are represented by feature vectors (FV); (2) inflexions are represented by angles (Angle) of peak points [Huang et al 2014];…”

Section: Compressed Time Series Representationmentioning

confidence: 99%

Supervised Anomaly Detection in Uncertain Pseudoperiodic Data Streams

Sun

Wang

et al. 2016

ACM Trans. Internet Technol.

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Online mining abnormal period patterns from multiple medical sensor data streams

Cited by 20 publications

References 18 publications

Use of machine learning to analyse routinely collected intensive care unit data: a systematic review

Use of machine learning to analyse routinely collected intensive care unit data: a systematic review

A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation

Supervised Anomaly Detection in Uncertain Pseudoperiodic Data Streams

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