期刊
JOURNAL OF BIOMEDICAL INFORMATICS
卷 126, 期 -, 页码 -出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jbi.2021.103975
关键词
Non-invasive hemoglobin measurement; Evolution trend; Nonparametric model; Robust locally estimated scatterplot smoothing; (LOESS) method; Kernel regression
资金
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
This article proposes a method to improve the accuracy of the SpHb measurement device using statistical nonparametric models. The method is evaluated through cross validation and shows a significant improvement in accuracy compared to the original measurements. Analysis of patient demographics and initial medical condition indicates that they do not have a significant effect on accuracy. The proposed method has high potential to support transfusion decision-making and continuous monitoring of hemoglobin concentration.
Uncontrolled hemorrhage is a leading cause of preventable death among patients with trauma. Early recognition of hemorrhage can aid in the decision to administer blood transfusion and improve patient outcomes. To provide real-time measurement and continuous monitoring of hemoglobin concentration, the non-invasive and continuous hemoglobin (SpHb) measurement device has drawn extensive attention in clinical practice. However, the accuracy of such a device varies in different scenarios, so the use is not yet widely accepted. This article focuses on using statistical nonparametric models to improve the accuracy of SpHb measurement device by considering measurement bias among instantaneous measurements and individual evolution trends. In the proposed method, the robust locally estimated scatterplot smoothing (LOESS) method and the Kernel regression model are considered to address those issues. Overall performance of the proposed method was evaluated by cross validation, which showed a substantial improvement in accuracy with an 11.3% reduction of standard deviation, 23.7% reduction of mean absolute error, and 28% reduction of mean absolute percentage error compared to the original measurements. The effects of patient demographics and initial medical condition were analyzed and deemed to not have a significant effect on accuracy. Because of its high accuracy, the proposed method is highly promising to be considered to support transfusion decision-making and continuous monitoring of hemoglobin concentration. The method also has promise for similar advancement of other diagnostic devices in healthcare.
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