Time-series deep learning and conformal prediction for improved sepsis diagnosis in primarily Non-ICU hospitalized patients.

Journal: Computers in biology and medicine
Published Date:

Abstract

PURPOSE: Sepsis, a life-threatening condition from an uncontrolled immune response to infection, is a leading cause of in-hospital mortality. Early detection is crucial, yet traditional diagnostic methods, like SIRS and SOFA, often fail to identify sepsis in non-ICU settings where monitoring is less frequent. Recent machine learning models offer new possibilities but lack generalizability and suffer from high false alarm rates.

Authors

  • Shaunak Dalal
    Department of Anesthesiology and Perioperative Medicine, 500 University Dr, Hershey, 17033, PA, USA. Electronic address: shadalal101@gmail.com.
  • Ahad Khaleghi Ardabili
    Department of Anesthesiology and Perioperative Medicine, 500 University Dr, Hershey, 17033, PA, USA. Electronic address: akhaleghiardabili@pennstatehealth.psu.edu.
  • Anthony S Bonavia
    Department of Anesthesiology and Perioperative Medicine, 500 University Dr, Hershey, 17033, PA, USA; Critical Illness and Sepsis Research Center, 500 University Dr, Hershey, 17033, PA, USA. Electronic address: abonavia@pennstatehealth.psu.edu.