Summary

for people ages 18 years and up (full criteria)
healthy people welcome
study started
estimated completion

Description

Summary

Machine learning is a powerful method for creating clinical decision support (CDS) tools, but it requires training labels which reflect the desired alert behavior. In the Phase I work for this project, investigators have developed an encoding software called HindSight that examines discharged patients' electronic health records (EHR), identifies clinicians' sepsis treatment decisions and patient outcomes, and passes these labeled examples to an online algorithm for retraining InSight, a machine-learning-based CDS tool for real-time sepsis prediction. Although HindSight has been shown to be successful in improving the performance of InSight in retrospective work, it has yet to be validated in prospective settings; therefore, in this project, the clinical utility of HindSight will be assessed through a multicenter randomized controlled trial (RCT).

Official Title

Using Clinical Treatment Data in a Machine Learning Approach for Sepsis Detection

Keywords

Sepsis Severe Sepsis Septic Shock Dascena Machine learning algorithm Toxemia HindSight InSight

Eligibility

You can join if…

Open to people ages 18 years and up

  • During the study period, all patients over the age of 18 presenting to the emergency department or admitted to an inpatient unit at the participating facilities will automatically be enrolled in the trial, until the enrollment target for the study is met

You CAN'T join if...

  • Patients under the age of 18

Details

Status
not yet accepting patients
Start Date
Completion Date
(estimated)
Sponsor
Dascena
ID
NCT04005001
Phase
Phase 2
Study Type
Interventional
Last Updated