Detection of correct and incorrect measurements in real-time continuous glucose monitoring systems by applying a postprocessing support vector machine.
IEEE Trans Biomed Eng
; 60(7): 1891-9, 2013 Jul.
Article
em En
| MEDLINE
| ID: mdl-23380841
ABSTRACT
Support vector machines (SVMs) are an attractive option for detecting correct and incorrect measurements in real-time continuous glucose monitoring systems (RTCGMSs), because their learning mechanism can introduce a postprocessing strategy for imbalanced datasets. The proposed SVM considers the geometric mean to obtain a more balanced performance between sensitivity and specificity. To test this approach, 23 critically ill patients receiving insulin therapy were monitored over 72 h using an RTCGMS, and a dataset of 537 samples, classified according to International Standards Organization (ISO) criteria (372 correct and 165 incorrect measurements), was obtained. The results obtained were promising for patients with septic shock or with sepsis, for which the proposed system can be considered as reliable. However, this approach cannot be considered suitable for patients without sepsis.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Glicemia
/
Algoritmos
/
Diagnóstico por Computador
/
Máquina de Vetores de Suporte
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Hiperglicemia
/
Insulina
Tipo de estudo:
Diagnostic_studies
/
Guideline
Limite:
Aged
/
Female
/
Humans
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Male
/
Middle aged
Idioma:
En
Ano de publicação:
2013
Tipo de documento:
Article