AIMC Topic: Algorithms

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A Data-Driven Approach to Predicting Recreational Activity Participation Using Machine Learning.

Research quarterly for exercise and sport
With the popularity of recreational activities, the study aimed to develop prediction models for recreational activity participation and explore the key factors affecting participation in recreational activities. A total of 12,712 participants, exc...

Multi-label classification of retinal diseases based on fundus images using Resnet and Transformer.

Medical & biological engineering & computing
Retinal disorders are a major cause of irreversible vision loss, which can be mitigated through accurate and early diagnosis. Conventionally, fundus images are used as the gold diagnosis standard in detecting retinal diseases. In recent years, more a...

A topological description of loss surfaces based on Betti Numbers.

Neural networks : the official journal of the International Neural Network Society
In the context of deep learning models, attention has recently been paid to studying the surface of the loss function in order to better understand training with methods based on gradient descent. This search for an appropriate description, both anal...

FDAA: A feature distribution-aware transferable adversarial attack method.

Neural networks : the official journal of the International Neural Network Society
In recent years, the research on transferable feature-level adversarial attack has become a hot spot due to attacking unknown deep neural networks successfully. But the following problems limit its transferability. Existing feature disruption methods...

GRAM: An interpretable approach for graph anomaly detection using gradient attention maps.

Neural networks : the official journal of the International Neural Network Society
Detecting unusual patterns in graph data is a crucial task in data mining. However, existing methods face challenges in consistently achieving satisfactory performance and often lack interpretability, which hinders our understanding of anomaly detect...

Machine learning and artificial intelligence within pediatric autoimmune diseases: applications, challenges, future perspective.

Expert review of clinical immunology
INTRODUCTION: Autoimmune disorders affect 4.5% to 9.4% of children, significantly reducing their quality of life. The diagnosis and prognosis of autoimmune diseases are uncertain because of the variety of onset and development. Machine learning can i...

Hybrid multimodal fusion for graph learning in disease prediction.

Methods (San Diego, Calif.)
Graph neural networks (GNNs) have gained significant attention in disease prediction where the latent embeddings of patients are modeled as nodes and the similarities among patients are represented through edges. The graph structure, which determines...

Simulating realistic patient profiles from pharmacokinetic models by a machine learning postprocessing correction of residual variability.

CPT: pharmacometrics & systems pharmacology
We address the problem of model misspecification in population pharmacokinetics (PopPK), by modeling residual unexplained variability (RUV) by machine learning (ML) methods in a postprocessing step after conventional model building. The practical pur...

Unsupervised Learning-Based Measurement of Ultrasonic Axial Transmission Velocity in Neonatal Bone.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
OBJECTIVES: To develop a robust algorithm for estimating ultrasonic axial transmission velocity from neonatal tibial bone, and to investigate the relationships between ultrasound velocity and neonatal anthropometric measurements as well as clinical b...

Applying generative AI with retrieval augmented generation to summarize and extract key clinical information from electronic health records.

Journal of biomedical informatics
BACKGROUND: Malnutrition is a prevalent issue in aged care facilities (RACFs), leading to adverse health outcomes. The ability to efficiently extract key clinical information from a large volume of data in electronic health records (EHR) can improve ...