Improving Accuracy of Noninvasive Hemoglobin Monitors: A Functional Regression Model for Streaming SpHb Data.
IEEE Trans Biomed Eng
; 66(3): 759-767, 2019 03.
Article
em En
| MEDLINE
| ID: mdl-30010545
ABSTRACT
OBJECTIVE:
The purpose of this paper is to develop a method for improving the accuracy of SpHb monitors, which are noninvasive hemoglobin monitoring tools, leading to better critical care protocols in trauma care.METHODS:
The proposed method is based on fitting smooth spline functions to SpHb measurements collected over a time window and then using a functional regression model to predict the true HgB value for the end of the time window.RESULTS:
The accuracy of the proposed method is compared to traditional methods. The mean absolute error between the raw SpHb measurements and the gold standard hemoglobin measurements was 1.26 g/Dl. The proposed method reduced the mean absolute error to 1.08 g/Dl. [1]Conclusion:
Fitting a smooth function to SpHb measurements improves the accuracy of Hgb predictions.SIGNIFICANCE:
Accurate prediction of current and future HgB levels can lead to sophisticated decision models that determine the optimal timing and amount of blood product transfusions.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Hemoglobinas
/
Oximetria
/
Monitorização Fisiológica
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
IEEE Trans Biomed Eng
Ano de publicação:
2019
Tipo de documento:
Article