Key result
An ensemble machine learning model combining extreme gradient boosting and random forest detected the need for blood transfusion using physiological signals with 92.7% accuracy and an AUROC of 0.977.
Why the study?
Detecting the need for blood transfusion is time-consuming and sometimes not easily diagnosed, such as in internal bleeding.
Does an ensemble machine learning model using continuous physiological signals accurately detect the need for blood transfusion in trauma intensive care patients?
Observational (n=40)
No
Does an ensemble machine learning model using continuous physiological signals accurately detect the need for blood transfusion in trauma intensive care patients?
An ensemble machine learning model utilizing continuous physiological signals demonstrated high accuracy and discriminative ability for detecting the need for blood transfusion in trauma intensive care patients.
Supports ML-based transfusion prediction in trauma ICU; hypothesis-generating and requires prospective validation before clinical use.
Adequate oxygen in red blood cells carrying through the body to the heart and brain is important to maintain life. For those patients requiring blood, blood transfusion is a common procedure in which donated blood or blood components are given through an intravenous line. However, detecting the need for blood transfusion is time-consuming and sometimes not easily diagnosed, such as internal bleeding. This study considered physiological signals such as electrocardiogram (ECG), photoplethysmogram (PPG), blood pressure, oxygen saturation (SpO2), and respiration, and proposed the machine learning model to detect the need for blood transfusion accurately. For the model, this study extracted 14 features from the physiological signals and used an ensemble approach combining extreme gradient boosting and random forest. The model was evaluated by a stratified five-fold cross-validation: the detection accuracy and area under the receiver operating characteristics were 92.7% and 0.977, respectively.
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Ko et al. (2023) conducted an observational in Trauma requiring blood transfusion (n=40). Ensemble machine learning model (XGBoost and Random Forest) vs. Single machine learning models was evaluated on Detection of blood transfusion need (AUROC 0.977). An ensemble machine learning model combining extreme gradient boosting and random forest detected the need for blood transfusion using physiological signals with 92.7% accuracy and an AUROC of 0.977.
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