AIMC Topic: Machine Learning

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Interpretable machine learning-based prediction of 28-day mortality in ICU patients with sepsis: a multicenter retrospective study.

Frontiers in cellular and infection microbiology
BACKGROUND: Sepsis is a major cause of mortality in intensive care units (ICUs) and continues to pose a significant global health challenge, with sepsis-related deaths contributing substantially to the overall burden on healthcare systems worldwide. ...

Longitudinal twin growth discordance patterns and adverse perinatal outcomes.

American journal of obstetrics and gynecology
BACKGROUND: Growth discordance in twin pregnancies is associated with increased perinatal morbidity and mortality, yet the patterns of discordance progression and the utility of Doppler assessments remain underinvestigated.

Machine learning assisted multi-signal nanozyme sensor array for the antioxidant phenolic compounds intelligent recognition.

Food chemistry
Identifying antioxidant phenolic compounds (APs) in food plays a crucial role in understanding their biological functions and associated health benefits. Here, a bifunctional Cu-1,3,5-benzenetricarboxylic acid (Cu-BTC) nanozyme was successfully prepa...

Alkenyl pheromones: Raman spectroscopic analysis, DFT modeling, and machine learning for stereoisomerism evaluation.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Alkenyl pheromones are a class of insect sex pheromones that are characterized by the presence of one or more double bonds, which can be either in the E(trans) or Z(cis) configuration. This structural variation is essential in mating, as it influence...

Prediction of Deoxynivalenol contamination in wheat kernels and flour based on visible near-infrared spectroscopy, feature selection and machine learning modelling.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Contamination of wheat by the mycotoxin Deoxynivalenol (DON), produced by Fusarium fungi, poses significant challenges to the quality of crop yield and food safety. Visible and near-infrared (vis-NIR) spectroscopy has emerged as a promising, non-dest...

Abductive multi-instance multi-label learning for periodontal disease classification with prior domain knowledge.

Medical image analysis
Machine learning is widely used in dentistry nowadays, offering efficient solutions for diagnosing dental diseases, such as periodontitis and gingivitis. Most existing methods for diagnosing periodontal diseases follow a two-stage process. Initially,...

Diagnostic and prognostic perspectives of Fabry disease via fiber evanescent wave spectroscopy advanced by machine learning.

Biosensors & bioelectronics
Fabry disease (FD) is a rare disorder resulting from a genetic mutation characterized by the accumulation of sphingolipids in various cells throughout the human body, leading to progressive and irreversible organ damage, particularly in males. Geneti...

Searching to extrapolate embedding for out-of-graph node representation learning.

Neural networks : the official journal of the International Neural Network Society
Out-of-graph node representation learning aims at learning about newly arrived nodes for a dynamic graph. It has wide applications ranging from community detection, recommendation system to malware detection. Although existing methods can be adapted ...

Artificial Intelligence, Machine Learning and Big Data in Radiation Oncology.

Hematology/oncology clinics of North America
This review explores the applications of artificial intelligence and machine learning (AI/ML) in radiation oncology, focusing on computer vision (CV) and natural language processing (NLP) techniques. We examined CV-based AI/ML in digital pathology an...

Rad4XCNN: A new agnostic method for post-hoc global explanation of CNN-derived features by means of Radiomics.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In recent years, machine learning-based clinical decision support systems (CDSS) have played a key role in the analysis of several medical conditions. Despite their promising capabilities, the lack of transparency in AI mode...