AI Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

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Precision and Robust Models on Healthcare Institution Federated Learning for Predicting HCC on Portal Venous CT Images.

IEEE journal of biomedical and health informatics
Hepatocellular carcinoma (HCC), the most common type of liver cancer, poses significant challenges in detection and diagnosis. Medical imaging, especially computed tomography (CT), is pivotal in non-invasively identifying this disease, requiring subs...

MPCNN: A Novel Matrix Profile Approach for CNN-based Single Lead Sleep Apnea in Classification Problem.

IEEE journal of biomedical and health informatics
Sleep apnea (SA) is a significant respiratory condition that poses a major global health challenge. Deep Learning (DL) has emerged as an efficient tool for the classification problem in electrocardiogram (ECG)-based SA diagnoses. Despite these advanc...

DSFE: Decoding EEG-Based Finger Motor Imagery Using Feature-Dependent Frequency, Feature Fusion and Ensemble Learning.

IEEE journal of biomedical and health informatics
Accurate decoding finger motor imagery is essential for fine motor control using EEG signals. However, decoding finger motor imagery is particularly challenging compared with ordinary motor imagery. This paper proposed a novel EEG decoding method of ...

Enhancing Major Depressive Disorder Diagnosis With Dynamic-Static Fusion Graph Neural Networks.

IEEE journal of biomedical and health informatics
Major Depressive Disorder (MDD) is a debilitating, complex mental condition with unclear mechanisms hindering diagnostic progress. Research links MDD to abnormal brain connectivity using functional magnetic resonance imaging (fMRI). Yet, existing fMR...

A Novel Unsupervised Machine Learning Approach to Assess Postural Dynamics in Euthymic Bipolar Disorder.

IEEE journal of biomedical and health informatics
Bipolar disorder (BD) is a mood disorder with different phases alternating between euthymia, manic or hypomanic episodes, and depressive episodes. While motor abnormalities are commonly seen during depressive or manic episodes, not much attention has...

Cooperating Graph Neural Networks With Deep Reinforcement Learning for Vaccine Prioritization.

IEEE journal of biomedical and health informatics
This study explores the vaccine prioritization strategy to reduce the overall burden of the pandemic when the supply is limited. Existing vaccine distribution methods focus on macro-level or simplified micro-level assuming homogeneous behavior within...

Exploring the Impact of Fine-Tuning the Wav2vec2 Model in Database-Independent Detection of Dysarthric Speech.

IEEE journal of biomedical and health informatics
Many acoustic features and machine learning models have been studied to build automatic detection systems to distinguish dysarthric speech from healthy speech. These systems can help to improve the reliability of diagnosis. However, speech recorded f...

Modeling 3D Cardiac Contraction and Relaxation With Point Cloud Deformation Networks.

IEEE journal of biomedical and health informatics
Global single-valued biomarkers, such as ejection fraction, are widely used in clinical practice to assess cardiac function. However, they only approximate the heart's true 3D deformation process, thus limiting diagnostic accuracy and the understandi...

A Computational Framework for Predicting Novel Drug Indications Using Graph Convolutional Network With Contrastive Learning.

IEEE journal of biomedical and health informatics
Inferring potential drug indications plays a vital role in the drug discovery process. It can be time-consuming and costly to discover novel drug indications through biological experiments. Recently, graph learning-based methods have gained popularit...

DCNet: A Self-Supervised EEG Classification Framework for Improving Cognitive Computing-Enabled Smart Healthcare.

IEEE journal of biomedical and health informatics
Cognitive computing endeavors to construct models that emulate brain functions, which can be explored through electroencephalography (EEG). Developing precise and robust EEG classification models is crucial for advancing cognitive computing. Despite ...