AIMC Topic: Humans

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An improved sample selection framework for learning with noisy labels.

PloS one
Deep neural networks have powerful memory capabilities, yet they frequently suffer from overfitting to noisy labels, leading to a decline in classification and generalization performance. To address this issue, sample selection methods that filter ou...

A machine-learning model for prediction of Acinetobacter baumannii hospital acquired infection.

PloS one
BACKGROUND: Acinetobacter baumanni infection is a leading cause of morbidity and mortality in the Intensive Care Unit (ICU). Early recognition of patients at risk for infection allows early proper treatment and is associated with improved outcomes. T...

Machine learning and game theory for cyber governance: Enhancing public opinion and regional sustainable development.

PloS one
Cyberspace is emerging as a critical living environment, significantly influencing sustainable human development. Internet public opinion is a crucial aspect of cyberspace governance, serving as the most important form of expressing popular will. How...

Understanding veterinary practitioners' responses to adverse events using a combined grounded theory and netnographic natural language processing approach.

PloS one
Support that mitigates the detrimental impact of adverse events on human healthcare practitioners is underpinned by an understanding of their experiences. This study used a mixed methods approach to understand veterinary practitioners' responses to a...

Deep learning based predictive modeling to screen natural compounds against TNF-alpha for the potential management of rheumatoid arthritis: Virtual screening to comprehensive in silico investigation.

PloS one
Rheumatoid arthritis (RA) affects an estimated 0.1% to 2.0% of the world's population, leading to a substantial impact on global health. The adverse effects and toxicity associated with conventional RA treatment pathways underscore the critical need ...

Topology aware multitask cascaded U-Net for cerebrovascular segmentation.

PloS one
Cerebrovascular segmentation is a crucial preliminary task for many computer-aided diagnosis tools dealing with cerebrovascular pathologies. Over the last years, deep learning based methods have been widely applied to this task. However, classic deep...

Celebrating 15 years of Psychotraumatology - a future with generative AI?

European journal of psychotraumatology
The was launched in 2010. In this editorial, we review the journal's developments over the past 15 years, and discuss some of the current ethical challenges in scientific publishing, including the impact of generative AI. How can we responsibly use ...

Comparing performances of french orthopaedic surgery residents with the artificial intelligence ChatGPT-4/4o in the French diploma exams of orthopaedic and trauma surgery.

Orthopaedics & traumatology, surgery & research : OTSR
INTRODUCTION: This study evaluates the performance of ChatGPT, particularly its versions 4 and 4o, in answering questions from the French orthopedic and trauma surgery exam (Diplôme d'Études Spécialisées, DES), compared to the results of French ortho...

Enhancing ergonomics in E-waste disassembly: the impact of collaborative robotics on muscle activation and coordination.

Ergonomics
Disassembly, as a part of the electronic waste (e-waste) management process, is a labour-intensive task. The emergence of collaborative robots (cobots) provides a robotic solution to reduce the human efforts during disassembly. This study evaluated m...

Predicting progression-free survival in sarcoma using MRI-based automatic segmentation models and radiomics nomograms: a preliminary multicenter study.

Skeletal radiology
OBJECTIVES: Some sarcomas are highly malignant, associated with high recurrence despite treatment. This multicenter study aimed to develop and validate a radiomics signature to estimate sarcoma progression-free survival (PFS).