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Machine Learning Platforms to Predict 30-day Mortality After Emergency Laparotomy

NCT05828914 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 5000

Last updated 2023-04-25

No results posted yet for this study

Summary

This study seeks to utilise retrospective patient data to train machine learning algorithms to predict the short term mortality and morbidity after an emergency laparotomy.

Data will be collected via the Electronic Health records system at the Queen Mary Hospital Hong Kong. Machine learning models will be compared and the best-performing one will be explored for further optimization and deployment. Upon completion, we hope that this platform will aid clinicians to identify high risk patients and aid clinical decisions and peri-operative planning, with the aim to reduce mortality and morbidity in this high risk procedure.

Conditions

  • Mortality Rate

Sponsors & Collaborators

  • The University of Hong Kong

    lead OTHER

Principal Investigators

  • Michael Garnet Irwin, M.B. Ch.B · The University of Hong Kong

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-04-28
Primary Completion
2024-07-31
Completion
2025-07-31

Countries

  • China

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05828914 on ClinicalTrials.gov