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Found 11 papers, 8 papers with code
Mitigating Overconfidence in Out-of-Distribution Detection by Capturing Extreme Activations
Detecting out-of-distribution (OOD) instances is crucial for the reliable deployment of machine learning models in real-world scenarios.
Causal prediction models for medication safety monitoring: The diagnosis of vancomycin-induced acute kidney injury
Here, we pioneer a causal modeling approach using observational data to estimate a lower bound of the PC (PC$_{low}$).
Fixing confirmation bias in feature attribution methods via semantic match
Feature attribution methods have become a staple method to disentangle the complex behavior of black box models.
Semantic match: Debugging feature attribution methods in XAI for healthcare
Despite valid concerns, we argue that existing criticism on the viability of post-hoc local explainability methods throws away the baby with the bathwater by generalizing a problem that is specific to image data.
Explainable Artificial Intelligence (XAI)
Feature Importance
+1
Why we do need Explainable AI for Healthcare
The recent spike in certified Artificial Intelligence (AI) tools for healthcare has renewed the debate around adoption of this technology.
Out-of-Distribution Detection for Medical Applications: Guidelines for Practical Evaluation
Detection of Out-of-Distribution (OOD) samples in real time is a crucial safety check for deployment of machine learning models in the medical field.
A pragmatic approach to estimating average treatment effects from EHR data: the effect of prone positioning on mechanically ventilated COVID-19 patients
Despite the recent progress in the field of causal inference, to date there is no agreed upon methodology to glean treatment effect estimation from observational data.
Know Your Limits: Uncertainty Estimation with ReLU Classifiers Fails at Reliable OOD Detection
A crucial requirement for reliable deployment of deep learning models for safety-critical applications is the ability to identify out-of-distribution (OOD) data points, samples which differ from the training data and on which a model might underperform.
Trust Issues: Uncertainty Estimation Does Not Enable Reliable OOD Detection On Medical Tabular Data
When deploying machine learning models in high-stakes real-world environments such as health care, it is crucial to accurately assess the uncertainty concerning a model's prediction on abnormal inputs.
Uncertainty estimation for classification and risk prediction on medical tabular data
In a data-scarce field such as healthcare, where models often deliver predictions on patients with rare conditions, the ability to measure the uncertainty of a model's prediction could potentially lead to improved effectiveness of decision support tools and increased user trust.
Bayesian Modelling in Practice: Using Uncertainty to Improve Trustworthiness in Medical Applications
In this work we show how Bayesian modelling and the predictive uncertainty that it provides can be used to mitigate risk of misguided prediction and to detect out-of-domain examples in a medical setting.
