AIMC Topic: Machine Learning

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[Machine learning models established to distinguish OA and RA based on immune factors in the knee joint fluid].

Xi bao yu fen zi mian yi xue za zhi = Chinese journal of cellular and molecular immunology
Objective Based on 25 indicators including immune factors, cell count classification, and smear results of the knee joint fluid, machine learning models were established to distinguish between osteoarthritis (OA) and rheumatoid arthritis (RA). Method...

Getting Started with Machine Learning for Experimental Biochemists and Other Molecular Scientists.

Current protocols
Machine learning (ML) is rapidly gaining traction in many areas of experimental molecular science for elucidating relationships and patterns in large or complex data sets. Historically, ML was largely the preserve of those with specialized training i...

Machine learning to improve HIV screening using routine data in Kenya.

Journal of the International AIDS Society
INTRODUCTION: Optimal use of HIV testing resources accelerates progress towards ending HIV as a global threat. In Kenya, current testing practices yield a 2.8% positivity rate for new diagnoses reported through the national HIV electronic medical rec...

Utilizing Machine Learning to Predict Liver Allograft Fibrosis by Leveraging Clinical and Imaging Data.

Clinical transplantation
BACKGROUND AND AIM: Liver transplant (LT) recipients may succumb to graft-related pathologies, contributing to graft fibrosis (GF). Current methods to diagnose GF are limited, ranging from procedural-related complications to low accuracy. With recent...

Dynamic and Static Structure-Function Coupling With Machine Learning for the Early Detection of Alzheimer's Disease.

Human brain mapping
The progression of Alzheimer's disease (AD) involves complex changes in brain structure and function that are driven by their interaction, making structure-function coupling (SFC) a valuable indicator for early detection of AD. Static SFC refers to t...

Machine Learning-Based Immuno-Inflammatory Index Integrating Clinical Characteristics for Predicting Coronary Artery Plaque Rupture.

Immunity, inflammation and disease
BACKGROUND: Coronary artery plaque rupture (PR) is closely associated with immune-inflammatory responses. The systemic inflammatory index (SII) and the systemic inflammatory response index (SIRI) have shown potential in predicting the occurrence of P...

Expanding interpretability through complexity reduction in machine learning-based modelling of cardiovascular disease: A myocardial perfusion imaging PET/CT prognostic study.

European journal of clinical investigation
BACKGROUND: Machine learning-based analysis can be used in myocardial perfusion imaging data to improve risk stratification and the prediction of major adverse cardiovascular events for patients with suspected or established coronary artery disease. ...

The role of artificial intelligence in cardiovascular research: Fear less and live bolder.

European journal of clinical investigation
BACKGROUND: Artificial intelligence (AI) has captured the attention of everyone, including cardiovascular (CV) clinicians and scientists. Moving beyond philosophical debates, modern cardiology cannot overlook AI's growing influence but must actively ...

Machine learning in cardiovascular risk assessment: Towards a precision medicine approach.

European journal of clinical investigation
Cardiovascular diseases remain the leading cause of global morbidity and mortality. Validated risk scores are the basis of guideline-recommended care, but most scores lack the capacity to integrate complex and multidimensional data. Limitations inher...

Machine Learning and Deep Learning in Detection of Neonatal Seizures: A Systematic Review.

Journal of evaluation in clinical practice
BACKGROUND: Neonatal seizures are one of the most prevalent clinical manifestations of neurological conditions, requiring urgent intervention and detection. Machine learning (ML) and Deep Learning (DL) is an emerging promising tool for detecting and ...