AI Medical Compendium

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

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Insight Extraction From E-Health Bookings by Means of Hypergraph and Machine Learning.

IEEE journal of biomedical and health informatics
New technologies are transforming medicine, and this revolution starts with data. Usually, health services within public healthcare systems are accessed through a booking centre managed by local health authorities and controlled by the regional gover...

MLNet: Metaheuristics-Based Lightweight Deep Learning Network for Cervical Cancer Diagnosis.

IEEE journal of biomedical and health informatics
One of the leading causes of cancer-related deaths among women is cervical cancer. Early diagnosis and treatment can minimize the complications of this cancer. Recently, researchers have designed and implemented many deep learning-based automated cer...

HDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysis.

IEEE journal of biomedical and health informatics
Synthetic digital twins based on medical data accelerate the acquisition, labelling and decision making procedure in digital healthcare. A core part of digital healthcare twins is model-based data synthesis, which permits the generation of realistic ...

MLP-Like Model With Convolution Complex Transformation for Auxiliary Diagnosis Through Medical Images.

IEEE journal of biomedical and health informatics
Medical images such as facial and tongue images have been widely used for intelligence-assisted diagnosis, which can be regarded as the multi-label classification task for disease location (DL) and disease nature (DN) of biomedical images. Compared w...

IChrom-Deep: An Attention-Based Deep Learning Model for Identifying Chromatin Interactions.

IEEE journal of biomedical and health informatics
Identification of chromatin interactions is crucial for advancing our knowledge of gene regulation. However, due to the limitations of high-throughput experimental techniques, there is an urgent need to develop computational methods for predicting ch...

Design and Evaluation of Deep Learning Models for Continuous Acute Pain Detection Based on Phasic Electrodermal Activity.

IEEE journal of biomedical and health informatics
The current method for assessing pain in clinical practice is subjective and relies on self-reported scales. An objective and accurate method of pain assessment is needed for physicians to prescribe the proper medication dosage, which could reduce ad...

DeepTPpred: A Deep Learning Approach With Matrix Factorization for Predicting Therapeutic Peptides by Integrating Length Information.

IEEE journal of biomedical and health informatics
The abuse of traditional antibiotics has led to increased resistance of bacteria and viruses. Efficient therapeutic peptide prediction is critical for peptide drug discovery. However, most of the existing methods only make effective predictions for o...

Learning-Based Multimodal Information Fusion and Behavior Recognition of Vascular Interventionists' Operating Skills.

IEEE journal of biomedical and health informatics
The operating skills of vascular interventionists have an important impact on the effect of surgery. However, current research on behavior recognition and skills learning of interventionists' operating skills is limited. In this study, an innovative ...

A Deep Learning Model for Automatic Segmentation of Intraparenchymal and Intraventricular Hemorrhage for Catheter Puncture Path Planning.

IEEE journal of biomedical and health informatics
Intracerebral hemorrhage is the subtype of stroke with the highest mortality rate, especially when it also causes secondary intraventricular hemorrhage. The optimal surgical option for intracerebral hemorrhage remains one of the most controversial ar...

Semantic-Aware Contrastive Learning for Multi-Object Medical Image Segmentation.

IEEE journal of biomedical and health informatics
Medical image segmentation, or computing voxel-wise semantic masks, is a fundamental yet challenging task in medical imaging domain. To increase the ability of encoder-decoder neural networks to perform this task across large clinical cohorts, contra...