Search Results for author:
Found 6 papers, 4 papers with code
Predicting adverse outcomes following catheter ablation treatment for atrial fibrillation
Results: Out of a total of 3285 patients in the cohort, 177 (5. 3%) experienced the composite outcome (heart failure, stroke, cardiac arrest, death) and 167 (5. 1%) experienced major bleeding events after catheter ablation treatment.
Continuous Treatment Recommendation with Deep Survival Dose Response Function
We propose a general formulation for continuous treatment recommendation problems in settings with clinical survival data, which we call the Deep Survival Dose Response Function (DeepSDRF).
Incorporating Uncertainty in Learning to Defer Algorithms for Safe Computer-Aided Diagnosis
LDU was evaluated on the diagnosis of myocardial infarction (using discharge summaries), the diagnosis of any comorbidities (using structured data), and the diagnosis of pleural effusion and pneumothorax (using chest x-rays), and compared with 'learning to defer without uncertainty information' (LD) and 'direct triage by uncertainty' (DT) methods.
Dynamic prediction of time to event with survival curves
With the ever-growing complexity of primary health care system, proactive patient failure management is an effective way to enhancing the availability of health care resource.
CDS -- Causal Inference with Deep Survival Model and Time-varying Covariates
Causal inference in longitudinal observational health data often requires the accurate estimation of treatment effects on time-to-event outcomes in the presence of time-varying covariates.
Targeted Estimation of Heterogeneous Treatment Effect in Observational Survival Analysis
The aim of clinical effectiveness research using repositories of electronic health records is to identify what health interventions 'work best' in real-world settings.
