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Using Computers to Help Doctors Know If Lung Patients Will Get Better in the Hospitals Special Care Unit

Using Machine Learning to Build Predictive Models on Patients with ARDS in medical Intensive Care Unit - NIL

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2024/10/075928
Enrollment
562
Registered
2024-10-25
Start date
Unknown
Completion date
Unknown
Last updated
2024-11-11

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Health Condition 1: J80- Acute respiratory distress syndrome

Interventions

Intervention1: nil: nil Control Intervention1: nil: nil

Sponsors

Department of healthcare and hospital management
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: Records of all Adult patients admitted in ICU 1 , ICU 2 and ICU 3 more than 48 hours from August 2021 to August 2024. Record of all patients with mild , moderate, and severe Acute Respiratory Distress Syndrome (ARDS) admitted in ICU 1 , ICU 2 and ICU 3 more than 48 hours from August 2021 to August 2024 .

Exclusion criteria

Exclusion criteria: All patients who are not admitted more than 48 hours. Patient not diagnosed with a disease .

Design outcomes

Primary

MeasureTime frame
Predictive model for ARDs patients in intensive Care UnitTimepoint: Retrospective Cohort Study—

Secondary

MeasureTime frame
helps clinicians to predict unfavorable medical conditions and their possible outcomes, produces reports, maneuver recommendations, or alarms in a timely mannerTimepoint: Retrospective cohort Study—

Countries

India

Contacts

Public ContactDr Nada Thasneem

Prasanna School Of Public Health

usha.rani@manipal.edu8310214742

Outcome results

None listed

Source: CTRI (via WHO ICTRP) · Data processed: Feb 4, 2026