首页|Case Western Reserve University Reports Findings in Sepsis (Machine learning interpretability methods to characterize the importance of hematologic biomarkers in prognosticating patients with suspected infection)
Case Western Reserve University Reports Findings in Sepsis (Machine learning interpretability methods to characterize the importance of hematologic biomarkers in prognosticating patients with suspected infection)
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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 originating in Cleveland, Ohio, by NewsRx journalists, research stated, “To evaluate the effectiveness of Monocyte Distribution Width (MDW) in predicting sepsis outcomes in emergency department (ED) patients compared to other hematologic parameter s and vital signs, and to determine whether routine parameters could substitute MDW in machine learning models. We conducted a retrospective analysis of data from 10,229 ED patients admitted to a large regional safety-net hospital in Clevel and, Ohio who had suspected infections and developed sepsis-associated poor outcomes.”
ClevelandOhioUnited StatesNorth and Central AmericaBiomarkersBlood Diseases and ConditionsBloodstream InfectionCyborgsDiagnostics and ScreeningEmerging TechnologiesHealth and MedicineMachine LearningSepsisSepticemia
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