NCT07121309

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

Establishment of a Postoperative Delirium Risk Prediction Model for Elderly Hip Fracture Patients Based on Machine Learning Algorithms

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 901 (actual)
• Sponsor: Second Affiliated Hospital of Soochow University · Academic / Other
• Sex: All
• Age: 60 Years
• Healthy volunteers: Accepted

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

Timeline

• Start date: 2024-10-17
• Primary completion: 2025-03-03
• Completion: 2025-03-03
• First posted: 2025-08-13
• Last updated: 2025-08-13

Locations

1 site across 1 country: China