Zobrazeno 1 - 10
of 71
pro vyhledávání: '"Strobl, Eric V."'
Autor:
Lasko, Thomas A., Still, John M., Li, Thomas Z., Mota, Marco Barbero, Stead, William W., Strobl, Eric V., Landman, Bennett A., Maldonado, Fabien
Insufficiently precise diagnosis of clinical disease is likely responsible for many treatment failures, even for common conditions and treatments. With a large enough dataset, it may be possible to use unsupervised machine learning to define clinical
Externí odkaz:
http://arxiv.org/abs/2402.05802
The rising popularity of artificial intelligence in healthcare is highlighting the problem that a computational model achieving super-human clinical performance at its training sites may perform substantially worse at new sites. In this perspective,
Externí odkaz:
http://arxiv.org/abs/2311.04787
Autor:
Strobl, Eric V.
Accurately inferring the root causes of disease from sequencing data can improve the discovery of novel therapeutic targets. However, existing root causal inference algorithms require perfectly measured continuous random variables. Single cell RNA se
Externí odkaz:
http://arxiv.org/abs/2307.05338
Autor:
Strobl, Eric V.
Root causes of disease intuitively correspond to root vertices that increase the likelihood of a diagnosis. This description of a root cause nevertheless lacks the rigorous mathematical formulation needed for the development of computer algorithms de
Externí odkaz:
http://arxiv.org/abs/2305.17574
Autor:
Strobl, Eric V., Lasko, Thomas A.
Root causal analysis seeks to identify the set of initial perturbations that induce an unwanted outcome. In prior work, we defined sample-specific root causes of disease using exogenous error terms that predict a diagnosis in a structural equation mo
Externí odkaz:
http://arxiv.org/abs/2210.15340
Autor:
Strobl, Eric V., Lasko, Thomas A.
Complex diseases are caused by a multitude of factors that may differ between patients even within the same diagnostic category. A few underlying root causes may nevertheless initiate the development of disease within each patient. We therefore focus
Externí odkaz:
http://arxiv.org/abs/2205.13085
Autor:
Strobl, Eric V., Lasko, Thomas A.
Complex diseases are caused by a multitude of factors that may differ between patients. As a result, hypothesis tests comparing all patients to all healthy controls can detect many significant variables with inconsequential effect sizes. A few highly
Externí odkaz:
http://arxiv.org/abs/2205.11627
Autor:
Strobl, Eric V., Lasko, Thomas A.
Randomized clinical trials eliminate confounding but impose strict exclusion criteria that limit recruitment to a subset of the population. Observational datasets are more inclusive but suffer from confounding -- often providing overly optimistic est
Externí odkaz:
http://arxiv.org/abs/2111.13229
Autor:
Strobl, Eric V., Lasko, Thomas A.
We consider estimating the conditional average treatment effect for everyone by eliminating confounding and selection bias. Unfortunately, randomized clinical trials (RCTs) eliminate confounding but impose strict exclusion criteria that prevent sampl
Externí odkaz:
http://arxiv.org/abs/2105.00455
Publikováno v:
In Computers in Biology and Medicine March 2024 171
