Raphael Poulain
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View article: Enabling Scalable Evaluation of Bias Patterns in Medical LLMs
Enabling Scalable Evaluation of Bias Patterns in Medical LLMs Open
Large language models (LLMs) have shown impressive potential in helping with numerous medical challenges. Deploying LLMs in high-stakes applications such as medicine, however, brings in many concerns. One major area of concern relates to b…
View article: Aligning (Medical) LLMs for (Counterfactual) Fairness
Aligning (Medical) LLMs for (Counterfactual) Fairness Open
Large Language Models (LLMs) have emerged as promising solutions for a variety of medical and clinical decision support applications. However, LLMs are often subject to different types of biases, which can lead to unfair treatment of indiv…
View article: Fairness-Optimized Synthetic EHR Generation for Arbitrary Downstream Predictive Tasks
Fairness-Optimized Synthetic EHR Generation for Arbitrary Downstream Predictive Tasks Open
Among various aspects of ensuring the responsible design of AI tools for healthcare applications, addressing fairness concerns has been a key focus area. Specifically, given the wide spread of electronic health record (EHR) data and their …
View article: Bias patterns in the application of LLMs for clinical decision support: A comprehensive study
Bias patterns in the application of LLMs for clinical decision support: A comprehensive study Open
Large Language Models (LLMs) have emerged as powerful candidates to inform clinical decision-making processes. While these models play an increasingly prominent role in shaping the digital landscape, two growing concerns emerge in healthca…
View article: Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods
Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods Open
Developing AI tools that preserve fairness is of critical importance, specifically in high-stakes applications such as those in healthcare. However, health AI models' overall prediction performance is often prioritized over the possible bi…
View article: Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods
Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods Open
Developing AI tools that preserve fairness is of critical importance, specifically in high-stakes applications such as those in healthcare. However, health AI models' overall prediction performance is often prioritized over the possible bi…
View article: An Extensive Data Processing Pipeline for MIMIC-IV.
An Extensive Data Processing Pipeline for MIMIC-IV. Open
An increasing amount of research is being devoted to applying machine learning methods to electronic health record (EHR) data for various clinical purposes. This growing area of research has exposed the challenges of the accessibility of E…
View article: Few-Shot Learning with Semi-Supervised Transformers for Electronic Health Records.
Few-Shot Learning with Semi-Supervised Transformers for Electronic Health Records. Open
With the growing availability of Electronic Health Records (EHRs), many deep learning methods have been developed to leverage such datasets in medical prediction tasks. Notably, transformer-based architectures have proven to be highly effe…
View article: Flexible-Window Predictions on Electronic Health Records
Flexible-Window Predictions on Electronic Health Records Open
Various types of machine learning techniques are available for analyzing electronic health records (EHRs). For predictive tasks, most existing methods either explicitly or implicitly divide these time-series datasets into predetermined obs…
View article: An Extensive Data Processing Pipeline for MIMIC-IV
An Extensive Data Processing Pipeline for MIMIC-IV Open
An increasing amount of research is being devoted to applying machine learning methods to electronic health record (EHR) data for various clinical purposes. This growing area of research has exposed the challenges of the accessibility of E…
View article: Transformer-based Multi-target Regression on Electronic Health Records for Primordial Prevention of Cardiovascular Disease
Transformer-based Multi-target Regression on Electronic Health Records for Primordial Prevention of Cardiovascular Disease Open
Machine learning algorithms have been widely used to capture the static and temporal patterns within electronic health records (EHRs). While many studies focus on the (primary) prevention of diseases, primordial prevention (preventing the …