AIMC Topic: Humans

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The development of an EU-wide nutrition and physical activity expert knowledge base to support a personalised mobile application across various EU population groups.

Nutrition bulletin
A healthy lifestyle comprising regular physical activity and an adequate diet is imperative for the prevention of non-communicable diseases such as hypertension and some cancers. Advances in information computer technology offer the opportunity to pr...

A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis.

Health care management science
Proactive analysis of patient pathways helps healthcare providers anticipate treatment-related risks, identify outcomes, and allocate resources. Machine learning (ML) can leverage a patient's complete health history to make informed decisions about f...

Smart Biosensor for Breast Cancer Survival Prediction Based on Multi-View Multi-Way Graph Learning.

Sensors (Basel, Switzerland)
Biosensors play a crucial role in detecting cancer signals by orchestrating a series of intricate biological and physical transduction processes. Among various cancers, breast cancer stands out due to its genetic underpinnings, which trigger uncontro...

Embedded Machine Learning System for Muscle Patterns Detection in a Patient with Shoulder Disarticulation.

Sensors (Basel, Switzerland)
The integration of artificial intelligence (AI) models in the classification of electromyographic (EMG) signals represents a significant advancement in the design of control systems for prostheses. This study explores the development of a portable sy...

BiLSTM- and CNN-Based m6A Modification Prediction Model for circRNAs.

Molecules (Basel, Switzerland)
m6A methylation, a ubiquitous modification on circRNAs, exerts a profound influence on RNA function, intracellular behavior, and diverse biological processes, including disease development. While prediction algorithms exist for mRNA m6A modifications...

HCA-DAN: hierarchical class-aware domain adaptive network for gastric tumor segmentation in 3D CT images.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: Accurate segmentation of gastric tumors from CT scans provides useful image information for guiding the diagnosis and treatment of gastric cancer. However, automated gastric tumor segmentation from 3D CT images faces several challenges. T...

Deformable registration of magnetic resonance images using unsupervised deep learning in neuro-/radiation oncology.

Radiation oncology (London, England)
PURPOSE: Accurate deformable registration of magnetic resonance imaging (MRI) scans containing pathologies is challenging due to changes in tissue appearance. In this paper, we developed a novel automated three-dimensional (3D) convolutional U-Net ba...

Lipoproteins and metabolites in diagnosing and predicting Alzheimer's disease using machine learning.

Lipids in health and disease
BACKGROUND: Alzheimer's disease (AD) is a chronic neurodegenerative disorder that poses a substantial economic burden. The Random forest algorithm is effective in predicting AD; however, the key factors influencing AD onset remain unclear. This study...

Development, comparison, and internal validation of prediction models to determine the visual prognosis of patients with open globe injuries using machine learning approaches.

BMC medical informatics and decision making
INTRODUCTION: Open globe injuries (OGI) represent a main preventable reason for blindness and visual impairment, particularly in developing countries. The goal of this study is evaluating key variables affecting the prognosis of open globe injuries a...

Machine learning models on a web application to predict short-term postoperative outcomes following anterior cervical discectomy and fusion.

BMC musculoskeletal disorders
BACKGROUND: The frequency of anterior cervical discectomy and fusion (ACDF) has increased up to 400% since 2011, underscoring the need to preoperatively anticipate adverse postoperative outcomes given the procedure's expanding use. Our study aims to ...