Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler
of its great success is the availability of abundant and high-quality data for building machine …

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 …

Clinical trials are indispensable in developing new treatments, but they face obstacles in
patient recruitment and retention, hindering the enrollment of necessary participants. To …

Liver transplantation is a life-saving procedure for patients with end-stage liver disease.
There are two main challenges in liver transplant: finding the best matching patient for a …

Electronic phenotyping is a fundamental task that identifies the special group of patients,
which plays an important role in precision medicine in the era of digital health. Phenotyping …

Organ transplant is the essential treatment method for some end-stage diseases, such as
liver failure. Analyzing the post-transplant cause of death (CoD) after organ transplant …

Feature preprocessing, which transforms raw input features into numerical representations,
is a crucial step in automated machine learning (AutoML) systems. However, the existing …

Artificial intelligence models represented in machine learning algorithms are promising tools
for risk assessment used to guide clinical and other health care decisions. Machine learning …

Parkinson's disease (PD), a neurodegenerative disorder, often manifests as speech and
voice dysfunction. While utilizing voice data for PD detection has great potential in clinical …

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