AIMC Topic: Databases, Factual

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AdamGraph: Adaptive Attention-Modulated Graph Network for EEG Emotion Recognition.

IEEE transactions on cybernetics
The underlying time-variant and subject-specific brain dynamics lead to inconsistent distributions in electroencephalogram (EEG) topology and representations within and between individuals. However, current works primarily align the distributions of ...

Enhancing forensic shoeprint analysis: Application of the Shoe-MS algorithm to challenging evidence.

Science & justice : journal of the Forensic Science Society
Quantitative assessment of pattern evidence is a challenging task, particularly in the context of forensic investigations where the accurate identification of sources and classification of items in evidence are critical. Emerging deep learning approa...

Initial seizure episodes risk factors identification during hospitalization of ICU patients: A retrospective analysis of the eICU collaborative research database.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
BACKGROUND: We aimed to identify risk factors for initial seizure episodes in ICU patients using various machine learning algorithms.

Artificial intelligence prediction model for readmission after DIEP flap breast reconstruction based on NSQIP data.

Journal of plastic, reconstructive & aesthetic surgery : JPRAS
BACKGROUND: Readmissions following deep inferior epigastric perforator (DIEP) flap breast reconstruction represent a significant healthcare burden, yet current risk prediction methods lack precision in identifying high-risk patients. We developed a m...

Sharper insights: Adaptive ellipse-template for robust fovea localization in challenging retinal landscapes.

Computers in biology and medicine
Automated identification of retinal landmarks, particularly the fovea is crucial for diagnosing diabetic retinopathy and other ocular diseases. But accurate identification is challenging due to varying contrast, color irregularities, anatomical struc...

Artificial intelligence driven platform for rapid catalytic performance assessment of nanozymes.

Scientific reports
Traditional methods for synthesizing nanozymes are often time-consuming and complex, hindering efficiency. Artificial intelligence (AI) has the potential to simplify these processes, but there are very few dedicated nanozyme databases available, limi...

A comprehensive case study of deep learning on the detection of alpha thalassemia and beta thalassemia using public and private datasets.

Scientific reports
This study explores the performance of deep learning models, specifically Convolutional Neural Networks (CNN) and XGBoost, in predicting alpha and beta thalassemia using both public and private datasets. Thalassemia is a genetic disorder that impairs...

Early prediction of sepsis associated encephalopathy in elderly ICU patients using machine learning models: a retrospective study based on the MIMIC-IV database.

Frontiers in cellular and infection microbiology
BACKGROUND: Sepsis associated encephalopathy (SAE) is prevalent among elderly patients in the ICU and significantly affects patient prognosis. Due to the symptom similarity with other neurological disorders and the absence of specific biomarkers, ear...

Interplay of Spontaneous Reporting and Longitudinal Healthcare Databases for Signal Management: Position Statement from the Real-World Evidence and Big Data Special Interest Group of the International Society of Pharmacovigilance.

Drug safety
Signal management, defined as the set of activities from signal detection to recommendations for action, is conducted using different data sources and leveraging data from spontaneous reporting databases (SRDs), which represent the cornerstone of pha...

Transformer-based deep learning ensemble framework predicts autism spectrum disorder using health administrative and birth registry data.

Scientific reports
Early diagnosis and access to resources, support and therapy are critical for improving long-term outcomes for children with autism spectrum disorder (ASD). ASD is typically detected using a case-finding approach based on symptoms and family history,...