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Found 10 papers, 0 papers with code
Guardrails for avoiding harmful medical product recommendations and off-label promotion in generative AI models
Generative AI (GenAI) models have demonstrated remarkable capabilities in a wide variety of medical tasks.
Instability in clinical risk stratification models using deep learning
While it has been well known in the ML community that deep learning models suffer from instability, the consequences for healthcare deployments are under characterised.
Machine learning for dynamically predicting the onset of renal replacement therapy in chronic kidney disease patients using claims data
In this work, we present a machine learning model that dynamically identifies CKD patients at risk of requiring RRT up to one year in advance using only claims data.
Pain Detection with fNIRS-Measured Brain Signals: A Personalized Machine Learning Approach Using the Wavelet Transform and Bayesian Hierarchical Modeling with Dirichlet Process Priors
Currently self-report pain ratings are the gold standard in clinical pain assessment.
Detection of Real-world Driving-induced Affective State Using Physiological Signals and Multi-view Multi-task Machine Learning
This may lead to empathic automotive user interfaces that account for the driver's emotional state and influence the driver in order to improve safety.
Deep Reinforcement Learning for Optimal Critical Care Pain Management with Morphine using Dueling Double-Deep Q Networks
Our results demonstrate that reinforcement learning may be used to aid decision making in the intensive care setting by providing personalized pain management interventions.
Multi-task multiple kernel machines for personalized pain recognition from functional near-infrared spectroscopy brain signals
Currently there is no validated objective measure of pain.
Physiological and behavioral profiling for nociceptive pain estimation using personalized multitask learning
Pain is a subjective experience commonly measured through patient's self report.
Regularization approaches for support vector machines with applications to biomedical data
The support vector machine (SVM) is a widely used machine learning tool for classification based on statistical learning theory.
Multi-task Neural Networks for Personalized Pain Recognition from Physiological Signals
Pain is a complex and subjective experience that poses a number of measurement challenges.
