AIMC Topic: Humans

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Extremity Soft Tissue Sarcoma Reconstruction Nomograms: A Clinicoradiomic, Machine Learning-Powered Predictor of Postoperative Outcomes.

JCO clinical cancer informatics
PURPOSE: The choice of wound closure modality after limb-sparing extremity soft-tissue sarcoma (eSTS) resection is fraught with uncertainty. Leveraging machine learning and clinicoradiomic data, we developed Sarcoma Reconstruction Nomograms (SARCON),...

Challenges and Opportunities for Global Cervical Cancer Elimination: How Can We Build a Model for Other Cancers?

American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting
Cervical cancer remains a leading cause of cancer-related death among women globally, despite the availability of effective prevention through human papillomavirus (HPV) vaccination and HPV-based screening. This review explores the state-of-the-art t...

DeepSeek-AI-enhanced virtual reality training for mass casualty management: Leveraging machine learning for personalized instructional optimization.

PloS one
OBJECTIVE: This study aimed to evaluate the effectiveness of a virtual reality (VR) training system for mass casualty management, integrating artificial intelligence (AI) and machine learning (ML) to analyze trainee performance and error patterns. Th...

Advancing artificial intelligence applicability in endoscopy through source-agnostic camera signal extraction from endoscopic images.

PloS one
INTRODUCTION: Successful application of artificial intelligence (AI) in endoscopy requires effective image processing. Yet, the plethora of sources for endoscopic images, such as different processor-endoscope combinations or capsule endoscopy devices...

In-depth exploration of software defects and self-admitted technical debt through cutting-edge deep learning techniques.

PloS one
Most previous research focuses on finding Self-Admitted Technical Debt (SATD) or detecting bugs alone, rather to addressing the concurrent identification of both issues. These study investigations solely identify and classify the SATD or faults, with...

Tailored knowledge distillation with automated loss function learning.

PloS one
Knowledge Distillation (KD) is one of the most effective and widely used methods for model compression of large models. It has achieved significant success with the meticulous development of distillation losses. However, most state-of-the-art KD loss...

Forecasting monthly residential natural gas demand in two cities of Turkey using just-in-time-learning modeling.

PloS one
Natural gas (NG) is relatively a clean source of energy, particularly compared to fossil fuels, and worldwide consumption of NG has been increasing almost linearly in the last two decades. A similar trend can also be seen in Turkey, while another sim...

Identifying determinants of under-5 mortality in Bangladesh: A machine learning approach with BDHS 2022 data.

PloS one
BACKGROUND: Under-5 mortality in Bangladesh remains a critical indicator of public health and socio-economic development. Traditional methods often struggle to capture the complex, non-linear relationships influencing under-5 mortality. This study le...

Non-end-to-end adaptive graph learning for multi-scale temporal traffic flow prediction.

PloS one
Accurate traffic flow prediction is vital for intelligent transportation systems but presents significant challenges. Existing methods, however, have the following limitations: (1) insufficient exploration of interactions across different temporal sc...

An ensemble-based 3D residual network for the classification of Alzheimer's disease.

PloS one
Alzheimer's disease (AD) is a common type of dementia, with mild cognitive impairment (MCI) being a key precursor. Early MCI diagnosis is crucial for slowing AD progression, but distinguishing MCI from normal controls (NC) is challenging due to subtl...