Fault Diagnostic of Variance Shifts in Clinical Monitoring Using an Artificial Neural Network Input Gain Measurement Approximation (ANNIGMA)

Gunaratne, N, ABDOLLAHIAN, M and Huda, S 2018, 'Fault Diagnostic of Variance Shifts in Clinical Monitoring Using an Artificial Neural Network Input Gain Measurement Approximation (ANNIGMA)', in Shahram Latifi (ed.) Information Technology-New generations 15th international Conference on Information Technology (ITNG 2018), Las Vegas, United States, 16-18 April 2018, pp. 295-300.


Document type: Conference Paper
Collection: Conference Papers

Title Fault Diagnostic of Variance Shifts in Clinical Monitoring Using an Artificial Neural Network Input Gain Measurement Approximation (ANNIGMA)
Author(s) Gunaratne, N
ABDOLLAHIAN, M
Huda, S
Year 2018
Conference name ITNG 2018: New Generations
Conference location Las Vegas, United States
Conference dates 16-18 April 2018
Proceedings title Information Technology-New generations 15th international Conference on Information Technology (ITNG 2018)
Editor(s) Shahram Latifi
Publisher Springer
Place of publication Switzerland
Start page 295
End page 300
Total pages 6
Abstract Condition of a patient in an intensive care unit is assessed by monitoring multiple correlated variables with individual observations. Individual monitoring of variables leads to misdiagnosis. Therefore, variability of the correlated variables needs to be monitored simultaneously by deploying a multivariate control chart. Once the shift from the accepted range is detected, it is vital to identify the variables that are responsible for the variance shift detected by the chart. This will aid the medical practitioners to take the appropriate medical intervention to adjust the condition of the patient. In this paper, Multivariate Exponentially Weighted Moving Variance chart has been used as the variance shift identifier. Once the shift is detected, authors for the first time have used ANNIGMA to identify the variables responsible for variance shifts in the condition of the patient and rank the responsible variables in terms of the percentage of their contribution to the variance shift. The performance of the proposed ANNIGMA has been measured by computing average classification accuracy. A case study based on real data collected from ICU unit shows that ANNIGMA not only improve the diagnosis but also speed up the variable identification for the purpose of appropriate medical diagnosis.
Subjects Applied Statistics
Keyword(s) MEWMV chart
Neural Networks
Multivariate variability
clinical monitoring
univariate moving range chart
DOI - identifier 10.1007/978-3-319-77028-4_40
Copyright notice © Springer International Publishing AG, part of Springer Nature 2018, corrected publication 2018
ISBN 9783319770284
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