3 papers
stat.AP2026
Correcting heterogeneous diagnostic bias when developing clinical prediction models using causal hidden Markov models
Jose Benitez-Aurioles, Ricardo Silva, Brian McMillan +1
In routine care, individuals identified a priori as high-risk are usually tested for conditions more frequently. Protected attributes, such as sex or ethnicity may also determine t…
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…