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.
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| http://dx.doi.org/10.1109/TBME.2018.2856091 | DOI Listing |
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