Objectives Leveraging artificial intelligence (AI) in conjunction with electronic health records
(EHRs) holds transformative potential to improve healthcare. However, addressing bias in …

This review examines the recent developments in deep learning (DL) techniques applied to
multimodal fusion image segmentation for liver cancer. Hepatocellular carcinoma is a highly …

The emphasis on fairness in predictive healthcare modeling has increased in popularity as
an approach for overcoming biases in automated decision-making systems. The aim is to …

Predictive models in biomedicine need to ensure equitable and reliable outcomes for the
populations they are applied to. Unfortunately, biases in medical predictions can lead to …

Maternal health risk prediction is a critical aspect of public health. This paper proposes a
new parameter optimization method to improve maternal health risk prediction using the …

Objective: Mitigating algorithmic disparities is a critical challenge in healthcare research,
where ensuring equity and fairness is paramount. While large-scale healthcare data exist …

Liver transplantation often faces fairness challenges across subgroups defined by sensitive
attributes such as age group, gender, and race/ethnicity. Machine learning models for …

Deep learning (DL) has received increasing attention lately as a promising framework for
clinical applications in healthcare. However, many existing DL works focus on making …

Disease predictive modelling supports decision-making for policymakers and healthcare
providers, and aids in managing health conditions for individuals. Recent advances in deep …

Abstract Electronic Health Records (EHRs) has gained its increasing significance in modern
healthcare as its promising prospects in the application of machine learning. The …