Imbalanced Medical

Imbalanced medical data, characterized by a disproportionate representation of classes (e.g., disease vs. no disease), poses a significant challenge in developing accurate and equitable machine learning models for healthcare applications. Current research focuses on mitigating this imbalance through techniques like data augmentation (e.g., using generative adversarial networks or denoising diffusion probabilistic models), algorithmic adjustments (e.g., modified loss functions, Bayesian approaches, and ensemble methods), and preprocessing strategies (e.g., resampling, feature selection, and handling missing data). Addressing this imbalance is crucial for improving the reliability and fairness of AI-driven diagnostic and prognostic tools, ultimately leading to better patient care and more equitable healthcare outcomes.

Papers

September 16, 2024

Mitigating Sex Bias in Audio Data-driven COPD and COVID-19 Breathing Pattern Detection Models
Rachel Pfeifer, Sudip Vhaduri, James Eric Dietz
Absolute Stance Bias Gender Bias Demographic Bias Exhaled Breath Imbalanced Medical

August 5, 2024

Bayesian Kolmogorov Arnold Networks (Bayesian_KANs): A Probabilistic Approach to Enhance Accuracy and Interpretability
Masoud Muhammed Hassan
Inherent Interpretability Bayesian Inference Kolmogorov Arnold Network Bayesian Learning Prediction Uncertainty Probabilistic Approach Enhancing Accuracy Imbalanced Medical

May 30, 2024

Enhancing Performance for Highly Imbalanced Medical Data via Data Regularization in a Federated Learning Setting
Georgios Tsoumplekas, Ilias Siniosoglou, Vasileios Argyriou, Ioannis D. Moscholios, Panagiotis Sarigiannidis
Federated Learning System Performance Class Imbalance Regularization Model Disease Prediction Medical Datasets Imbalanced Medical

May 16, 2024

An Autoencoder and Generative Adversarial Networks Approach for Multi-Omics Data Imbalanced Class Handling and Classification
Ibrahim Al-Hurani, Abedalrhman Alkhateeb, Salama Ikki
Classification Code Latent Space Supervised Autoencoder Dimensionality Reduction Generative Adversarial Breast Cancer High Dimensional Data Imbalanced Medical Bladder Cancer

November 28, 2023

Text2Tree: Aligning Text Representation to the Label Tree Hierarchy for Imbalanced Medical Classification
Jiahuan Yan, Haojun Gao, Zhang Kai, Weize Liu, Danny Chen, Jian Wu, Jintai Chen
Text Classification Text Representation Medical Text Label Hierarchy Imbalanced Medical Label Tree

October 27, 2023

MCRAGE: Synthetic Healthcare Data for Fairness
Keira Behal, Jiayi Chen, Caleb Fikes, Sophia Xiao
Procedural Fairness Class Imbalance Medical Datasets Synthetic Electronic Health Record Synthetic Medical Data Imbalanced Medical

October 6, 2023

Segmented Harmonic Loss: Handling Class-Imbalanced Multi-Label Clinical Data for Medical Coding with Large Language Models
Surjya Ray, Pratik Mehta, Hongen Zhang, Ada Chaman, Jian Wang, Chung-Jen Ho, Michael Chiou, Tashfeen Suleman
Natural Language Processing Loss Function Multi Label Medical Coding Imbalanced Medical

September 30, 2023

Enhancing Mortality Prediction in Heart Failure Patients: Exploring Preprocessing Methods for Imbalanced Clinical Datasets
Hanif Kia, Mansour Vali, Hadi Sabahi
Mortality Prediction Clinical Datasets Preprocessing Method Heart Failure Imbalanced Medical

November 14, 2022

Machine Learning Performance Analysis to Predict Stroke Based on Imbalanced Medical Dataset
Yuru Jing
Machine Learning Imbalanced Datasets Stroke Detection Synthetic Minority Oversampling Technique Imbalanced Medical Algorithm Namely Kmeans

July 23, 2022

Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data
Zepeng Huo, Xiaoning Qian, Shuai Huang, Zhangyang Wang, Bobak J. Mortazavi
Training Model Risk Prediction Medical Datasets Aware Loss Imbalanced Medical Class Density

Imbalanced Medical

Papers

Mitigating Sex Bias in Audio Data-driven COPD and COVID-19 Breathing Pattern Detection Models

Bayesian Kolmogorov Arnold Networks (Bayesian_KANs): A Probabilistic Approach to Enhance Accuracy and Interpretability

Enhancing Performance for Highly Imbalanced Medical Data via Data Regularization in a Federated Learning Setting

An Autoencoder and Generative Adversarial Networks Approach for Multi-Omics Data Imbalanced Class Handling and Classification

Text2Tree: Aligning Text Representation to the Label Tree Hierarchy for Imbalanced Medical Classification

MCRAGE: Synthetic Healthcare Data for Fairness

Segmented Harmonic Loss: Handling Class-Imbalanced Multi-Label Clinical Data for Medical Coding with Large Language Models

Enhancing Mortality Prediction in Heart Failure Patients: Exploring Preprocessing Methods for Imbalanced Clinical Datasets

Machine Learning Performance Analysis to Predict Stroke Based on Imbalanced Medical Dataset

Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data