首页|Data on Sepsis Reported by Fei Ye and Colleagues (A customised down-sampling machine learning approach for sepsis prediction)
Data on Sepsis Reported by Fei Ye and Colleagues (A customised down-sampling machine learning approach for sepsis prediction)
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2024 FEB 27 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Blood Diseases and Conditions - Sepsis is the subject of a report. According to news reporting out of Eindhoven, Netherlands, by NewsRx editors, research stated, “Sepsis is a life-threatening condition in the ICU and requires treatment in time. Despite the accuracy of existing sepsis prediction models, insufficient focus on reducing alarms could worsen alarm fatigue and desensitisation in ICUs, potentially compromising patient safety.” Our news journalists obtained a quote from the research, “In this retrospective study, we aim to develop an accurate, robust, and readily deployable method in ICUs, only based on the vital signs and laboratory tests. Our method consists of a customised down-sampling process and a specific dynamic sliding window and XGBoost to offer sepsis prediction. The down-sampling process was applied to the retrospective data for training the XGBoost model. During the testing stage, the dynamic sliding window and the trained XGBoost were used to predict sepsis on the retrospective datasets, PhysioNet and FHC. With the filtered data from PhysioNet, our method achieved 80.74% accuracy (77.90% sensitivity and 84.42% specificity) and 83.95% (84.82% sensitivity and 82.00% specificity) on the test set of PhysioNet-A and PhysioNet-B, respectively. The AUC score was 0.89 for both datasets. On the FHC dataset, our method achieved 92.38% accuracy (88.37% sensitivity and 95.16% specificity) and 0.98 AUC score on the test set of FHC. Our results indicate that the down-sampling process and the dynamic sliding window with XGBoost brought robust and accurate performance to give sepsis prediction under various hospital settings.”
EindhovenNetherlandsEuropeBlood Diseases and ConditionsBloodstream InfectionCyborgsEmerging TechnologiesHealth and MedicineMachine LearningSepsisSepticemia
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