TY - JOUR
T1 - Improving Accuracy of Noninvasive Hemoglobin Monitors
T2 - A Functional Regression Model for Streaming SpHb Data
AU - Das, Devashish
AU - Pasupathy, Kalyan S.
AU - Haddad, Nadeem N.
AU - Hallbeck, M. Susan
AU - Zielinski, Martin D.
AU - Sir, Mustafa Y.
PY - 2019/3
Y1 - 2019/3
N2 - 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.
AB - 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.
KW - Functional regression
KW - improving accuracy
KW - noninvasive hemoglobin monitors
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U2 - 10.1109/TBME.2018.2856091
DO - 10.1109/TBME.2018.2856091
M3 - Article
C2 - 30010545
AN - SCOPUS:85049977826
VL - 66
SP - 759
EP - 767
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
SN - 0018-9294
IS - 3
M1 - 8410784
ER -