AIMC Topic: Machine Learning

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A Scoping Review of Machine-Learning Derived Radiomic Analysis of CT and PET Imaging to Investigate Atherosclerotic Cardiovascular Disease.

Tomography (Ann Arbor, Mich.)
BACKGROUND: Cardiovascular disease affects the carotid arteries, coronary arteries, aorta and the peripheral arteries. Radiomics involves the extraction of quantitative data from imaging features that are imperceptible to the eye. Radiomics analysis ...

Availability of Evidence for Predictive Machine Learning Algorithms in Primary Care: A Systematic Review.

JAMA network open
IMPORTANCE: The aging and multimorbid population and health personnel shortages pose a substantial burden on primary health care. While predictive machine learning (ML) algorithms have the potential to address these challenges, concerns include trans...

Low-pass whole genome sequencing of circulating tumor cells to evaluate chromosomal instability in triple-negative breast cancer.

Scientific reports
Chromosomal Instability (CIN) is a common and evolving feature in breast cancer. Large-scale Transitions (LSTs), defined as chromosomal breakages leading to gains or losses of at least 10 Mb, have recently emerged as a metric of CIN due to their stan...

A method for miRNA diffusion association prediction using machine learning decoding of multi-level heterogeneous graph Transformer encoded representations.

Scientific reports
MicroRNAs (miRNAs) are a key class of endogenous non-coding RNAs that play a pivotal role in regulating diseases. Accurately predicting the intricate relationships between miRNAs and diseases carries profound implications for disease diagnosis, treat...

Comparison of machine learning algorithms and multiple linear regression for live weight estimation of Akkaraman lambs.

Tropical animal health and production
This study was designed to predict the post-weaning weights of Akkaraman lambs reared on different farms using multiple linear regression and machine learning algorithms. The effect of factors the age of the dam, gender, type of lambing, enterprise, ...

Machine learning to identify precachexia and cachexia: a multicenter, retrospective cohort study.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
BACKGROUND: Detection of precachexia is important for the prevention and treatment of cachexia. However, how to identify precachexia is still a challenge.

Lateral cephalometric parameters among Arab skeletal classes II and III patients and applying machine learning models.

Clinical oral investigations
BACKGROUND: The World Health Organization considers malocclusion one of the most essential oral health problems. This disease influences various aspects of patients' health and well-being. Therefore, making it easier and more accurate to understand a...

Fuzzy lattices assisted EJAYA Q-learning for automated pulmonary diseases classification.

Biomedical physics & engineering express
This work proposes a novel technique called Enhanced JAYA (EJAYA) assisted Q-Learning for the classification of pulmonary diseases, such as pneumonia and tuberculosis (TB) sub-classes using chest x-ray images. The work introduces Fuzzy lattices forma...

Advancing Tau PET Quantification in Alzheimer Disease with Machine Learning: Introducing THETA, a Novel Tau Summary Measure.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Alzheimer disease (AD) exhibits spatially heterogeneous 3- or 4-repeat tau deposition across participants. Our overall goal was to develop an automated method to quantify the heterogeneous burden of tau deposition into a single number that would be c...