Real-time analysis of vital signs using incremental data stream mining techniques with a case study of ARDS under ICU treatment
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Author list: Fong S., Siu S.W.I., Zhou S., Chan J.H., Mohammed S., Fiaidhi J.
Publisher: American Scientific Publishers
Publication year: 2015
Volume number: 5
Issue number: 5
Start page: 1108
End page: 1115
Number of pages: 8
ISSN: 2156-7018
eISSN: 2156-7026
Languages: English-Great Britain (EN-GB)
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Abstract
Analysing data streams of vital signs has been a popular topic in research communities with techniques mainly focusing on detection, classification and prediction. One drawback for data classification/prediction is that the data mining model is built based on a full set of stationary data. Updating the model for sustaining the classification accuracy often needs the whole dataset including the evolving data to be accessed. This nature of model rebuilding dampers the possibility of mining vital signs in real-time and at high speed. Unfortunately, much of the past papers in the literature were based on traditional data mining models. In this paper, a data stream mining model which is flexible in configuring with different incremental data stream learning methods is tested as a real-time classification engine for mining vital data streams. A computer simulation experiment is conducted that is based on a case study of adult respiratory distress syndrome under twelve-hours of ICU treatment. The results indicate promising possibilities of performing real-time prediction by the proposed model. Copyright ฉ 2015 American Scientific Publishers All rights reserved.
Keywords
Data Stream Mining, Na๏ve Bayes, Optimized Very Fast Decision Tree, Vital Signs Analysis
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