A machine learning model using random forest analysis improves the prediction of mortality risk in hospitalized patients with cirrhosis.
Key Details
- 1Study analyzed data from 121 hospitals worldwide participating in the CLEARED consortium.
- 2Random forest AI model outperformed traditional risk prediction methods for hospitalized cirrhosis patients.
- 3Model maintained accuracy across both high- and low-income countries.
- 4External validation was performed using National U.S. veterans’ data.
- 5Effective risk stratification was possible with just 15 variables.
- 6The tool grouped patients by mortality risk, supporting clinical decision-making.
Why It Matters
This study highlights the growing potential for AI to enhance clinical prognostication, supporting triage and care prioritization. AI models like this can be adapted to radiology and imaging datasets to similarly improve patient management and operational workflows.
Source
EurekAlert
Related News
•EurekAlert
NIH-Backed AI Model Predicts Cancer Survival Using Single-Cell Data
Researchers have developed scSurvival, a machine learning tool that uses single-cell tumor data to accurately predict cancer patient survival and identify high-risk cell populations.
•EurekAlert
Deep Learning Pathomics Platform Improves Immunotherapy Prediction in Lung Cancer
A deep learning pathomics platform accurately predicts immunotherapy response in metastatic NSCLC using routine pathology slides.
•EurekAlert
AI Pathology Model Outperforms PD-L1 in Predicting NSCLC Immunotherapy Response
MD Anderson's Path-IO machine learning platform accurately predicts immunotherapy responses in metastatic non-small cell lung cancer, surpassing current biomarker standards.