NCT06775067

Incremental Dialysis Decision Model Based on Expert-Guided Machine Learning

Machine Learning Based on Expert Knowledge to Build and Validate a Decision Model for Incremental Dialysis

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 175 (actual)
• Sponsor: Huashan Hospital · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

This observational prospective study combined clinical expert knowledge with machine learning to develop and validate a predictive model for incremental hemodialysis decision-making. The aim of the predictive model is to assist clinicians in developing individualized incremental dialysis treatment plans to optimize patient outcomes.

Detailed description

By collecting patients' clinical and biochemical parameters and combining them with experts' judgments of dialysis timing and frequency, the model can dynamically assess patients' risk of needing to increase the frequency of dialysis, thus assisting physicians in formulating individualized incremental dialysis regimens to optimize dialysis outcomes and improve patients' prognosis.

Conditions

• End-stage Renal Disease

Timeline

• Start date: 2010-04-12
• Primary completion: 2024-06-28
• Completion: 2024-06-28
• First posted: 2025-01-14
• Last updated: 2025-01-14

Locations

1 site across 1 country: China