Rob Goedhart
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View article: Why we do need explainable AI for healthcare
Why we do need explainable AI for healthcare Open
The recent uptake in certified Artificial Intelligence (AI) tools for healthcare applications has renewed the debate around their adoption. Explainable AI, the sub-discipline promising to render AI devices more transparent and trustworthy,…
View article: Clinicians' Voice: Fundamental Considerations for XAI in Healthcare
Clinicians' Voice: Fundamental Considerations for XAI in Healthcare Open
Explainable AI (XAI) holds the promise of advancing the implementation and adoption of AI-based tools in practice, especially in high-stakes environments like healthcare. However, most of the current research lacks input from end users, an…
View article: Comparison of threshold tuning methods for predictive monitoring
Comparison of threshold tuning methods for predictive monitoring Open
Predictive monitoring techniques produce signals in case of a high predicted probability of an undesirable event, such as mortality, heart attacks, or machine failure. When using these predicted probabilities to classify the unknown outcom…
View article: Fixing confirmation bias in feature attribution methods via semantic match
Fixing confirmation bias in feature attribution methods via semantic match Open
Feature attribution methods have become a staple method to disentangle the complex behavior of black box models. Despite their success, some scholars have argued that such methods suffer from a serious flaw: they do not allow a reliable in…
View article: Optimized control charts using indifference regions
Optimized control charts using indifference regions Open
In statistical process monitoring, the CUSUM and EWMA control charts have received considerable attention because of their remarkable ability to detect small sustained shifts. In practice, small process variation and shifts are anticipated…
View article: Finding Regions of Counterfactual Explanations via Robust Optimization
Finding Regions of Counterfactual Explanations via Robust Optimization Open
Counterfactual explanations play an important role in detecting bias and improving the explainability of data-driven classification models. A counterfactual explanation (CE) is a minimal perturbed data point for which the decision of the m…
View article: Semantic match: Debugging feature attribution methods in XAI for healthcare
Semantic match: Debugging feature attribution methods in XAI for healthcare Open
The recent spike in certified Artificial Intelligence (AI) tools for healthcare has renewed the debate around adoption of this technology. One thread of such debate concerns Explainable AI (XAI) and its promise to render AI devices more tr…
View article: Why we do need Explainable AI for Healthcare
Why we do need Explainable AI for Healthcare Open
The recent spike in certified Artificial Intelligence (AI) tools for healthcare has renewed the debate around adoption of this technology. One thread of such debate concerns Explainable AI and its promise to render AI devices more transpar…
View article: Improved control chart performance using cautious parameter learning
Improved control chart performance using cautious parameter learning Open
Parameter estimation is an important topic in Statistical Process Monitoring, as inaccurate estimates may lead to undesirable control chart performance. Updating the control chart limits during the monitoring period reduces estimation unce…
View article: Monitoring proportions with two components of common cause variation
Monitoring proportions with two components of common cause variation Open
We propose a method for monitoring proportions when the in-control proportion and the sample sizes vary over time. Our approach is able to overcome some of the performance issues of other commonly used methods, as we demonstrate in this pa…
View article: On the design of control charts with guaranteed conditional performance under estimated parameters
On the design of control charts with guaranteed conditional performance under estimated parameters Open
When designing control charts the in‐control parameters are unknown, so the control limits have to be estimated using a Phase I reference sample. To evaluate the in‐control performance of control charts in the monitoring phase (Phase II), …
View article: A head-to-head comparison of the out-of-control performance of control charts adjusted for parameter estimation
A head-to-head comparison of the out-of-control performance of control charts adjusted for parameter estimation Open
When in-control process parameters are estimated, this can have a substantial effect on the control chart performance. In particular, it may lead to high false alarm rates for a large number of practitioners. In recent literature, control …
View article: Nonparametric control of the conditional performance in statistical process monitoring
Nonparametric control of the conditional performance in statistical process monitoring Open
Because the in-control distribution and parameters are generally unknown, control limits have to be estimated using a Phase I reference sample. Because different practitioners obtain different samples, their control limit estimates will va…
View article: The performance of control charts for large non‐normally distributed datasets
The performance of control charts for large non‐normally distributed datasets Open
Because of digitalization, many organizations possess large datasets. Furthermore, measurement data are often not normally distributed. However, when samples are sufficiently large, the central limit theorem may be used for the sample mean…
View article: Statistical control of Shewhart control charts
Statistical control of Shewhart control charts Open
Data availability has increased immensely in the past years, and so has the need for data analysis techniques. A key point of interest is often to use process data to detect changes in the underlying process. This applies to numerous envir…
View article: Shewhart control charts for dispersion adjusted for parameter estimation
Shewhart control charts for dispersion adjusted for parameter estimation Open
Several recent studies have shown that the number of Phase I samples required for a Phase II control chart with estimated parameters to perform properly may be prohibitively high. Looking for a more practical alternative, adjusting the con…
View article: Phase II control charts for monitoring dispersion when parameters are estimated
Phase II control charts for monitoring dispersion when parameters are estimated Open
Shewhart control charts are among the most popular control charts used to monitor process dispersion. To base these control charts on the assumption of known in-control process parameters is often unrealistic. In practice, estimates are us…
View article: Correction factors for Shewhart and control charts to achieve desired unconditional ARL
Correction factors for Shewhart and control charts to achieve desired unconditional ARL Open
In this paper we derive correction factors for Shewhart control charts that monitor individual observations as well as subgroup averages. In practice, the distribution parameters of the process characteristic of interest are unknown and, t…