51 citations · 95 across the 6 of their papers we have counts for
8 papers
Neural Fine-Gray: Monotonic neural networks for competing risks
Vincent Jeanselme, Chang Ho Yoon, Brian Tom +1
Time-to-event modelling, known as survival analysis, differs from standard regression as it addresses censoring in patients who do not experience the event of interest. Despite com…
Optimal risk-assessment scheduling for primary prevention of cardiovascular disease
Francesca Gasperoni, Christopher H. Jackson, Angela M. Wood +4
In this work, we introduce a personalised and age-specific Net Benefit function, composed of benefits and costs, to recommend optimal timing of risk assessments for cardiovascular…
Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness
Vincent Jeanselme, Maria De-Arteaga, Zhe Zhang +2
Machine learning risks reinforcing biases present in data and, as we argue in this work, in what is absent from data. In healthcare, societal and decision biases shape patterns in…
DeepJoint: Robust Survival Modelling Under Clinical Presence Shift
Vincent Jeanselme, Glen Martin, Niels Peek +3
Observational data in medicine arise as a result of the complex interaction between patients and the healthcare system. The sampling process is often highly irregular and itself co…
Sample Size Estimation using a Latent Variable Model for Mixed Outcome Co-Primary, Multiple Primary and Composite Endpoints
Martina McMenamin, Jessica K. Barrett, Anna Berglind +1
Mixed outcome endpoints that combine multiple continuous and discrete components to form co-primary, multiple primary or composite endpoints are often employed as primary outcome m…
Employing latent variable models to improve efficiency in composite endpoint analysis
Martina McMenamin, Jessica K. Barrett, Anna Berglind +1
Composite endpoints that combine multiple outcomes on different scales are common in clinical trials, particularly in chronic conditions. In many of these cases, patients will have…