activity
20242026
collaborators

6 papers

stat.ME2026

A flexible approach to sequential prediction under intervention

Matthew Sperrin, Bowen Jiang, Joyce Huang +2

We propose a causal predictive framework for estimating risk under preventative interventions. The Unexposed Mediator Model maintains mediators that are also predictors at their un…

stat.ME2026

Compatibility of Missing Data Handling Methods across the Stages of Producing Clinical Prediction Models

Antonia Tsvetanova, Matthew Sperrin, David A. Jenkins +7

Missing data is a challenge when developing, validating and deploying clinical prediction models (CPMs). Traditionally, decisions concerning missing data handling during CPM develo…

cs.LG2025

Prediction of Survival Outcomes under Clinical Presence Shift: A Joint Neural Network Architecture

Vincent Jeanselme, Glen Martin, Matthew Sperrin +3

Electronic health records arise from the complex interaction between patients and the healthcare system. This observation process of interactions, referred to as clinical presence,…

stat.ME2025

The risks of risk assessment: causal blind spots when using prediction models for treatment decisions

Nan van Geloven, Ruth H Keogh, Wouter van Amsterdam +12

Clinicians increasingly rely on prediction models to guide treatment choices. Most prediction models, however, are developed using observational data that include some patients who…

stat.AP2024

The continuous net benefit: Assessing the clinical utility of prediction models when informing a continuum of decisions

Jose Benitez-Aurioles, Laure Wynants, Niels Peek +3

Clinical prognostic models help inform decision-making by estimating a patient's risk of experiencing an outcome in the future. The net benefit is increasingly being used to assess…

stat.AP2024

Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity

Jose Benitez-Aurioles, Alice Joules, Irene Brusini +2

There are concerns about the fairness of clinical prediction models. 'Fair' models are defined as those for which their performance or predictions are not inappropriately influence…