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Paper Details

HeMA: A hierarchically enriched machine learning approach for managing false alarms in real time: A sepsis prediction case study.
Comput Biol Med
4
2021
patient, sepsis
Early Diagnosis, Humans, Machine Learning, Predictive Value of Tests, Sepsis
Author NameAffiliation
Zeyu LiuThe University of Tennessee
Anahita KhojandiThe University of Tennessee
Akram MohammedCenter for Biomedical Informatics-Pediatrics, The University of Tennessee Health Science Center
Xueping LiThe University of Tennessee
Lokesh K ChinthalaCenter for Biomedical Informatics-Pediatrics, The University of Tennessee Health Science Center
Robert L DavisCenter for Biomedical Informatics-Pediatrics, The University of Tennessee Health Science Center
Rishikesan KamaleswaranEmory University School of Medicine
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