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Found 5 papers, 1 papers with code
Uncertainty Quantification in Neural-Network Based Pain Intensity Estimation
As LossS outperforms, we assessed its performance in three different scenarios for pain assessment: (1) a generalized approach (single model for the entire population), (2) a personalized approach (separate model for each individual), and (3) a hybrid approach (separate model for each cluster of individuals).
Automatic pain recognition from Blood Volume Pulse (BVP) signal using machine learning techniques
In this study, we applied machine learning techniques on BVP signals to device a non-invasive modality for pain sensing.
Transformer Encoder with Multiscale Deep Learning for Pain Classification Using Physiological Signals
These results confirm that our approach can be utilized for automated classification of pain intensity using physiological signals to improve pain management and treatment.
Review and Analysis of Pain Research Literature through Keyword Co-occurrence Networks
Pain is a significant public health problem as the number of individuals with a history of pain globally keeps growing.
Learning to Identify Patients at Risk of Uncontrolled Hypertension Using Electronic Health Records Data
We develop machine learning models (logistic regression and recurrent neural networks) to stratify patients with respect to the risk of exhibiting uncontrolled hypertension within the coming three-month period.
