Continuous time Bayesian network classifiers

Stella, Fabio; Amer, Y. A.

doi:10.1016/j.jbi.2012.07.002

Cited by 32 publications

(28 citation statements)

References 12 publications

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“…Concept drift learners are able to monitor input data and adapt, when needed, the learners to new acquired data. Such approaches are particularly suitable to deal with data streams [61,62,63].…”

Section: Big Data Technologies: Software Algorithms and Architecturesmentioning

confidence: 99%

Big Data and Biomedical Informatics: A Challenging Opportunity

Bellazzi¹

2014

Yearb Med Inform

132

101

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Big data are receiving an increasing attention in biomedicine and healthcare. It is therefore important to understand the reason why big data are assuming a crucial role for the biomedical informatics community. The capability of handling big data is becoming an enabler to carry out unprecedented research studies and to implement new models of healthcare delivery. Therefore, it is first necessary to deeply understand the four elements that constitute big data, namely Volume, Variety, Velocity, and Veracity, and their meaning in practice. Then, it is mandatory to understand where big data are present, and where they can be beneficially collected. There are research fields, such as translational bioinformatics, which need to rely on big data technologies to withstand the shock wave of data that is generated every day. Other areas, ranging from epidemiology to clinical care, can benefit from the exploitation of the large amounts of data that are nowadays available, from personal monitoring to primary care. However, building big data-enabled systems carries on relevant implications in terms of reproducibility of research studies and management of privacy and data access; proper actions should be taken to deal with these issues. An interesting consequence of the big data scenario is the availability of new software, methods, and tools, such as map-reduce, cloud computing, and concept drift machine learning algorithms, which will not only contribute to big data research, but may be beneficial in many biomedical informatics applications. The way forward with the big data opportunity will require properly applied engineering principles to design studies and applications, to avoid preconceptions or over-enthusiasms, to fully exploit the available technologies, and to improve data processing and data management regulations.

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Section: Big Data Technologies: Software Algorithms and Architecturesmentioning

confidence: 99%

Big Data and Biomedical Informatics: A Challenging Opportunity

Bellazzi¹

2014

Yearb Med Inform

132

101

View full text Add to dashboard Cite

show abstract

“…In this case "Naïve Bayes-like" or "Augmented Transition Networks-like" models have been proposed [64]. In this case "Naïve Bayes-like" or "Augmented Transition Networks-like" models have been proposed [64].…”

Section: Building and Evaluating Data Mining Modelsmentioning

confidence: 99%

Engineering Principles in Biomedical Informatics

Bellazzi

Gabetta

Leonardi

2014

Methods in Biomedical Informatics

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“…This contribution is devoted to the analysis of gait disorders [8,9] and selected Parkinson's disease attributes [10][11][12] using the MS Kinect and three-dimensional modeling to detect movement patterns and to subject these features to a Bayesian classification [13,14]. Synchronized video-camera systems [15][16][17] ing.…”

Section: Introductionmentioning

confidence: 99%