Article

ML-based Pain Recognition Model Using Mixup Data Augmentation

2024-08-07

Abstract excerpt

Machine Learning (ML) has revolutionized healthcare by enhancing diagnostic capabilities because of its ability to analyze large datasets and detect minor patterns often overlooked by humans. This is beneficial, especially in pain recognition, where patient communication may be limited. However, ML models often face challenges such as memorization and sensitivity to adversarial examples. Regularization techniques...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: d93fe1fc-1f57-5bca-a6e5-adc038ff246e
DOI: 10.20944/preprints202408.0493.v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

ML-based Pain Recognition Model Using Mixup Data AugmentationDOI 10.20944/preprints202408.0493.v1Read

Beyond Subjective Measures: Systematic Review of Deep Learning in Chronic Pain: Modalities, Methods, and ApplicationsSemantic proximity Read

Can machine learning with data from wearable devices distinguish disease severity levels and generalise across patients? A pilot study in Mania and DepressionSemantic proximity Read

Explainable fNIRS Based Pain Decoding Under Pharmacological Conditions via Deep Transfer Learning ApproachSemantic proximity Read

EEG-Based Pain Detection Using Kernel Spectral Connectivity Network with Preserved Spatio-Frequency InterpretabilitySemantic proximity Read

On the use of variational autoencoders for biomedical data integrationSemantic proximity Read

Probing machine-learning classifiers using noise, bubbles, and reverse correlationSemantic proximity Read

Data Augmentation and Deep Learning Techniques to Improve Interface Noise Tolerance of Myoelectric Pattern Recognition ControllersSemantic proximity Read

Systematic Review of the Effectiveness of Machine Learning Algorithms for Classifying Pain Intensity, Phenotype or Treatment Outcomes Using Electroencephalogram Data.Semantic proximity Read

Deep embedded clustering generalisability and adaptation for mixed datatypes: two critical care cohortsSemantic proximity Read

AI-driven Diagnostic Processes and Comprehensive Multimodal Models in Pain MedicineSemantic proximity Read

Estimating the dimensionality of the manifold underlying multi-electrode neural recordingsSemantic proximity Read

A Survey of Contrastive Learning in Medical AI: Foundations, Biomedical Modalities, and Future DirectionsSemantic proximity Read