AI Medical Compendium Topic

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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...

Enhancing the performance of premature ventricular contraction detection in unseen datasets through deep learning with denoise and contrast attention module.

Computers in biology and medicine
Premature ventricular contraction (PVC) is a common and harmless cardiac arrhythmia that can be asymptomatic or cause palpitations and chest pain in rare instances. However, frequent PVCs can lead to more serious arrhythmias, such as atrial fibrillat...

XA4C: eXplainable representation learning via Autoencoders revealing Critical genes.

PLoS computational biology
Machine Learning models have been frequently used in transcriptome analyses. Particularly, Representation Learning (RL), e.g., autoencoders, are effective in learning critical representations in noisy data. However, learned representations, e.g., the...

A multi-stage transfer learning strategy for diagnosing a class of rare laryngeal movement disorders.

Computers in biology and medicine
BACKGROUND: It remains hard to directly apply deep learning-based methods to assist diagnosing essential tremor of voice (ETV) and abductor and adductor spasmodic dysphonia (ABSD and ADSD). One of the main challenges is that, as a class of rare laryn...

Incorporating entity-level knowledge in pretrained language model for biomedical dense retrieval.

Computers in biology and medicine
In recent years, pre-trained language models (PLMs) have dominated natural language processing (NLP) and achieved outstanding performance in various NLP tasks, including dense retrieval based on PLMs. However, in the biomedical domain, the effectiven...

Procedure code overutilization detection from healthcare claims using unsupervised deep learning methods.

BMC medical informatics and decision making
BACKGROUND: Fraud, Waste, and Abuse (FWA) in medical claims have a negative impact on the quality and cost of healthcare. A major component of FWA in claims is procedure code overutilization, where one or more prescribed procedures may not be relevan...

Cybersecurity in Internet of Medical Vehicles: State-of-the-Art Analysis, Research Challenges and Future Perspectives.

Sensors (Basel, Switzerland)
The "Internet-of-Medical-Vehicles (IOMV)" is one of the special applications of the Internet of Things resulting from combining connected healthcare and connected vehicles. As the IOMV communicates with a variety of networks along its travel path, it...

Behind the mask: a critical perspective on the ethical, moral, and legal implications of AI in ophthalmology.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: This narrative review aims to provide an overview of the dangers, controversial aspects, and implications of artificial intelligence (AI) use in ophthalmology and other medical-related fields.

A new architecture combining convolutional and transformer-based networks for automatic 3D multi-organ segmentation on CT images.

Medical physics
PURPOSE: Deep learning-based networks have become increasingly popular in the field of medical image segmentation. The purpose of this research was to develop and optimize a new architecture for automatic segmentation of the prostate gland and normal...

Cross-domain mechanism for few-shot object detection on Urine Sediment Image.

Computers in biology and medicine
Deep learning object detection networks require a large amount of box annotation data for training, which is difficult to obtain in the medical image field. The few-shot object detection algorithm is significant for an unseen category, which can be i...