Zobrazeno 1 - 10
of 83
pro vyhledávání: '"Díaz-Ordaz Karla"'
When estimating heterogeneous treatment effects, missing outcome data can complicate treatment effect estimation, causing certain subgroups of the population to be poorly represented. In this work, we discuss this commonly overlooked problem and cons
Externí odkaz:
http://arxiv.org/abs/2412.19711
Bridging the gap between internal and external validity is crucial for heterogeneous treatment effect estimation. Randomised controlled trials (RCTs), favoured for their internal validity due to randomisation, often encounter challenges in generalisi
Externí odkaz:
http://arxiv.org/abs/2405.20957
With the adoption of machine learning into routine clinical practice comes the need for Explainable AI methods tailored to medical applications. Shapley values have sparked wide interest for locally explaining models. Here, we demonstrate their inter
Externí odkaz:
http://arxiv.org/abs/2402.00072
Weighted average derivative effects (WADEs) are nonparametric estimands with uses in economics and causal inference. Debiased WADE estimators typically require learning the conditional mean outcome as well as a Riesz representer (RR) that characteris
Externí odkaz:
http://arxiv.org/abs/2308.05456
Publikováno v:
ICML 2022 Workshop on Interpretable Machine Learning in Healthcare
With the adoption of machine learning-based solutions in routine clinical practice, the need for reliable interpretability tools has become pressing. Shapley values provide local explanations. The method gained popularity in recent years. Here, we re
Externí odkaz:
http://arxiv.org/abs/2306.14698
Publikováno v:
International Conference on Machine Learning 2023
Predictive black-box models can exhibit high accuracy but their opaque nature hinders their uptake in safety-critical deployment environments. Explanation methods (XAI) can provide confidence for decision-making through increased transparency. Howeve
Externí odkaz:
http://arxiv.org/abs/2306.14672
Autor:
Tanner, Kamaryn, Keogh, Ruth H., Coupland, Carol A. C., Hippisley-Cox, Julia, Diaz-Ordaz, Karla
Over time, the performance of clinical prediction models may deteriorate due to changes in clinical management, data quality, disease risk and/or patient mix. Such prediction models must be updated in order to remain useful. Here, we investigate meth
Externí odkaz:
http://arxiv.org/abs/2305.00260
Publikováno v:
In Journal of Clinical Epidemiology November 2024 175
The recognition that personalised treatment decisions lead to better clinical outcomes has sparked recent research activity in the following two domains. Policy learning focuses on finding optimal treatment rules (OTRs), which express whether an indi
Externí odkaz:
http://arxiv.org/abs/2204.06030
Publikováno v:
Trials, Vol 12, Iss Suppl 1, p A19 (2011)
Externí odkaz:
https://doaj.org/article/ba45b8d71a514f65aad0ef3ad11314ef