AIMC Topic: Machine Learning

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Utilizing machine learning for predicting mortality in patients with heat-related illness who visited the emergency department.

International journal of medical informatics
BACKGROUND: In the context of climate change and global warming, heat-related illness (HRI) is anticipated to escalate and become a major concern. Patients with severe HRI primarily present to the emergency department (ED), but there are no predictio...

Application of machine learning approaches for estimating carbon dioxide absorption capacity of a variety of blended imidazolium-based ionic liquids.

Journal of molecular graphics & modelling
Ionic liquids (ILs) have gained attention in recent times as potentially effective absorbents for CO emissions owing to the number of their notable attributes, including reduced volatility, enhanced thermal consistency etc. Due to the number of chall...

Rosmarinic acid in Perilla frutescens L. as a potential adenosine deaminase inhibitor: Preparation, machine learning validation and binding mechanism study.

Food chemistry
Gout, a prevalent arthritic disease, can be mitigated by adenosine deaminase (ADA) inhibitors that reduce uric acid production. In this study, the extraction process of rosmarinic acid (RA) from Perilla frutescens L. (P. frutescens) was optimized, an...

SSSLN:Multivariate Time Series Forecasting via Collaborative Dynamic Graph Learning.

Neural networks : the official journal of the International Neural Network Society
Multivariate time series (MTS) forecasting has achieved notable progress through graph modeling. However, existing approaches often face two key challenges. First, traditional dynamic graph learning (DGL) methods typically maintain dynamic graphs dir...

Big data-driven target identification by machine learning: DRD2 as a therapeutic target for psoriasis.

Journal of dermatological science
BACKGROUND: The development of medical treatments has traditionally relied on researchers leveraging scientific knowledge to hypothesize disease mechanisms and identify therapeutic agents. However, the depletion of novel therapeutic targets has becom...

Multiscale insights into cornuside's effects on NAFLD: A cross-disciplinary integrating bioinformatics, computational chemistry, and machine learning.

Phytomedicine : international journal of phytotherapy and phytopharmacology
BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a complex metabolic disorder involving intertwined signaling pathways, posing challenges for targeted therapeutic interventions. Cornus Fructus (CF), a traditional medicinal herb, holds potenti...

Hybrid Neural network and machine learning models with improved optimization method for gut microbiome effects on the sleep quality in patients with endometriosis.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Endometriosis is a chronic gynecological condition known to affect the quality of life of millions of women globally, often manifesting with symptoms that impact sleep quality. Emerging evidence suggests a crucial role of th...

Altered static and dynamic functional network connectivity and combined Machine learning in asthma.

Neuroscience
Asthma is a reversible disease characterized by airflow limitation and chronic airway inflammation. Previous neuroimaging studies have shown structural and functional abnormalities in the brains of individuals with asthma. However, earlier research h...

Detection of β-Thalassemia trait from a heterogeneous population with red cell indices and parameters.

Computers in biology and medicine
BACKGROUND: India is home to about 42 million people with β-thalassemia trait (βTT) necessitating screening of βTT to stop spread of the disease. Over the years, researchers developed discrimination formulae based on red blood cell (RBC) parameters t...

Machine Learning to Predict Early Death Despite Pancreaticoduodenectomy.

The Journal of surgical research
INTRODUCTION: About 25% of patients undergoing pancreaticoduodenectomy (PD) for right-sided pancreatic ductal adenocarcinoma (PDAC) die within 1 y of diagnosis. These patients carry all the risks of significant morbidity with no survival advantage wh...