AIMC Topic: Humans

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Global burden and future trends of head and neck cancer: a deep learning-based analysis (1980-2030).

PloS one
BACKGROUND: Head and neck cancer (HNC) becomes a vital global health burden. Accurate assessment of the disease burden plays an essential role in setting health priorities and guiding decision-making.

Deep learning-based improved side-channel attacks using data denoising and feature fusion.

PloS one
Deep learning, as a high-performance data analysis method, has demonstrated superior efficiency and accuracy in side-channel attacks compared to traditional methods. However, many existing models enhance accuracy by stacking network layers, leading t...

Utilizing a deep learning model based on BERT for identifying enhancers and their strength.

PloS one
An enhancer is a specific DNA sequence typically located within a gene at upstream or downstream position and serves as a pivotal element in the regulation of eukaryotic gene transcription. Therefore, the recognition of enhancers is highly significan...

Enhancing student-centered walking environments on university campuses through street view imagery and machine learning.

PloS one
Campus walking environments significantly influence college students' daily lives and shape their subjective perceptions. However, previous studies have been constrained by limited sample sizes and inefficient, time-consuming methodologies. To addres...

The potential of evaluating shape drawing using machine learning for predicting high autistic traits.

PloS one
BACKGROUND: Children with high autistic traits often exhibit deficits in drawing, an important skill for social adaptability. Machine learning is a powerful technique for learning predictive models from movement data, so drawing processes and product...

CoHet4Rec: A recommendation for collaborative heterogeneous information networks.

PloS one
Recommender Systems (RS) aim to predict users' latent interests in items by learning embeddings from user-item graphs. Graph Neural Networks (GNNs) have significantly advanced RS by enabling the embedding of graph-structured data. However, relying so...

Machine learning approach for the prediction of 30-day mortality in patients with sepsis-associated delirium.

PloS one
This study aimed to develop models for predicting the 30-day mortality of sepsis-associated delirium (SAD) by multiple machine learning (ML) algorithms. On the whole, a cohort of 3,197 SAD patients were collected from the Medical Information Mart for...

Diagnostic performance of attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy for detecting osteoarthritis and rheumatoid arthritis from blood serum.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Osteoarthritis (OA) and rheumatoid arthritis (RA) are the two most common rheumatic diseases worldwide, causing pain and disability. Both conditions are highly heterogeneous, and their onset occurs insidiously with non-specific symptoms, so they are ...

Diagnostic performance of radiomics models for preoperative prediction of microsatellite instability status in endometrial cancer: a systematic review and meta-analysis.

Abdominal radiology (New York)
PURPOSE: Microsatellite instability (MSI), caused by defects in mismatch repair (MMR) genes, serves as a critical molecular biomarker with therapeutic implications for endometrial cancer (EC). This study aims to assess the diagnostic performance of r...

The Science of Statistical Practice.

Academic medicine : journal of the Association of American Medical Colleges
Emerging technologies, such as artificial intelligence, emphasize the importance of quantitative methods and their applications when conducting increasingly data-intensive research. The scientific discipline of statistical practice is critical for ac...