Sepsis sepsis, machine learning, emergency room
Conditions
Interventions
During the intervention period, the machine learning model would compute the real-time prediction probability of sepsis for the patients immediately after patient registration and the visiting data su
Experimental No treatment,No Intervention Other
AI - augmented decision support,Control
Sponsors
Chulalongkorn University
Eligibility
Sex/Gender
All
Age
18 Years to No maximum
Inclusion criteria
Inclusion criteria: 1. Age older than 18 years 2. Non-traumatic patient
Exclusion criteria
Exclusion criteria: 1. Pregnancy
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| time to antibiotic time since septic patient registered in emergency room to recieving antibiotic time ( minutes),Percents of sepsis patient that recieved antibiotic within 1 hour since patient registered in emergency room to discharge from emergency room Percents | — |
Secondary
| Measure | Time frame |
|---|---|
| length of stay in emergency room time since patient registered in emergency room to discharge from emergency room time (minutes),Inhospital mortality ince patient registered in emergency room to discharge from hospital Percents | — |
Countries
Thailand
Contacts
Public ContactNorawit Kijpaisalratana
Faculty of Medicine, Chulalongkorn University
Outcome results
None listed