MedTrace Logo

With the Development of Research, New Algorithms and Technologies Have Emerged, One of Which is Machine Learning. Machine Learning Can Extract Key Factors From Vast Amounts of Data, Identify Underlying Patterns, and Predict Future Trends. In Recent Years, Machine Learning Has Been Widely Used in

NCT07121309 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 901

Last updated 2025-08-13

No results posted yet for this study

Summary

The aim of this study is to construct a predictive model for postoperative delirium in elderly patients with hip fractures. The main question it answers is to construct a risk prediction model for hip fractures in the elderly through six machine learning methods, compare which method's model is better, and conduct external validation of the model's stability to provide a reference for the early clinical detection of postoperative delirium in elderly hip fracture patients.

The clinical data of elderly patients with hip fractures have been collected in clinical practice and the model has been constructed.

Conditions

  • Delirium

Sponsors & Collaborators

  • Second Affiliated Hospital of Soochow University

    lead OTHER

Eligibility

Min Age
60 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2024-10-17
Primary Completion
2025-03-03
Completion
2025-03-03

Countries

  • China

Study Locations

More Related Trials

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 NCT07121309 on ClinicalTrials.gov