Causal Machine Learning for LAAO in Patients with AF скачать в хорошем качестве

Causal Machine Learning for LAAO in Patients with AF

How can a causal machine learning framework act as a decision-supporting tool to select patients for left atrial appendage occlusion (LAAO)? In this short interview, Dr Xiaoxi Yao (Mayo Clinic, Rochester, MN, US) discusses a study investigating the causal forest model learning framework to identify characteristics in atrial fibrillation patients which would benefit most from LAAO. Dr Yao explains how the causal forest model estimates heterogeneous treatment effects and how it can be implemented in routine clinical practice. Dr Yao also highlights the considerations that must be taken when interpreting results from the model. Interview Questions: 1. Can you explain the clinical challenge of patient selection for LAAO? 2. Could you tell us about the causal machine learning framework used in this study? 3. How might this machine learning approach change current clinical decision-making for patients with atrial fibrillation? 4. What specific patient characteristics might contribute to the potential benefits of LAAO? 5. What further research or validation would you recommend based on these findings? Recorded remotely from Rochester, 2025. Visit Radcliffe Cardiology: https://www.radcliffecardiology.com/ This content is intended for healthcare professionals only. Radcliffe brings medical knowledge, insight and innovation to life for CV clinicians around the world, using our communications & creative expertise, our platforms and connections across the community to help transform theory into practice faster. Like us on Facebook:   / radcliffecardiology   Follow us on X: https://x.com/radcliffeCARDIO