Discovery and diagnosis of behavioral transitions in patient event streams

Robinson, William; Akhlaghi, Arash; Deng, Tianjie; Syed, Ali

doi:10.1145/2151163.2151167

Cited by 14 publications

(4 citation statements)

References 68 publications

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“…This interaction based approach has proven its feasibility with interactions. Another proof in specific domain is that Robin et al [18] in 2012 gave an discovery and diagnosis of behavioural transitions in patient event streams.…”

Section: Related Workmentioning

confidence: 99%

Requirements monitoring for Internetware: an interaction based approach

Chen

Liu

2013

Sci. China Inf. Sci.

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Implementation decision making for internetware driven by quality requirements SCIENCE CHINA Information Sciences 57, 072104 (2014); Model-based system configuration approach for Internetware SCIENCE CHINA Information Sciences 56, 082102 (2013); Eliciting dependability requirements: a control cases based approach SCIENCE CHINA Information Sciences 57, 012108 (2014); Modeling and verifying Web services driven by requirements: An ontology-based approach Science in China Series F-Information Sciences 49, 792 (2006);. RESEARCH PAPER .

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Section: Related Workmentioning

confidence: 99%

Requirements monitoring for Internetware: an interaction based approach

Chen

Liu

2013

Sci. China Inf. Sci.

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“…Kim et al (2004) proposed a multi-chart CUSUM change detection procedure for the detection of DOS attacks in network traffic. Robinson et al (2012) proposed a method for monitoring and detecting behavioral changes from an event stream of patient actions.…”

Section: Unique Applications Of the Slwe In Non-stationary Environmentsmentioning

confidence: 99%

On utilizing weak estimators to achieve the online classification of data streams

Tavasoli

Oommen

Yazidi

2019

Engineering Applications of Artificial Intelligence

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Classification, typically, deals with unique and distinct training and testing phases. This paper pioneers the concept when these phases are not so clearly well-defined. More specifically, we consider the case where the testing patterns can subsequently be considered as training patterns. The paradigm is further complicated because we assume that the class-conditional distributions of the features/classes are non-stationary, as in the case of most real-world applications. Specifically, we consider the model where the training phase is nonstationary and that it is, further, interleaved with the testing, and where it can be done online and in a real-time manner.We propose a novel online classifier for complex data streams which are generated from non-stationary stochastic properties. Instead of using a single training model with ''counters'' that maintain important data statistics, our online classifier scheme provides a real-time self-adjusting learning model. The learning model utilizes the multiplication-based update algorithm of the Stochastic Learning Weak Estimator (SLWE) at each time instant as a new labeled instance arrives. In this way, the data statistics are updated every time a new element is seen, without requiring that we have to rebuild the model when changes occur in the data distributions. Finally, and most importantly, the model operates with the understanding that the correct classes of previously-classified patterns become available at a later juncture subsequent to some time instances. This forces us to update the training set, the training model and the class conditional distributions as the testing proceeds.The results from rigorous empirical analysis on two-dimensional/multi-dimensional and binomial/ multinomial distributions are remarkable. We also report some results on two real-life datasets adapted to this model of computation, demonstrating the advantages of the novel scheme for both binomial and multinomial non-stationary distributions.

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“…[30]. Robinson et al [31] discuss the application of continuously applied real-time monitoring, such as process monitoring, for discovering process deviations, i.e., changes in user behavior, in a healthcare scenario.…”

Section: Related Workmentioning

confidence: 99%