AIMC Topic: Machine Learning

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An Intelligent System for Classifying Patient Complaints Using Machine Learning and Natural Language Processing: Development and Validation Study.

Journal of medical Internet research
BACKGROUND: Accurate classification of patient complaints is crucial for enhancing patient satisfaction management in health care settings. Traditional manual methods for categorizing complaints often lack efficiency and precision. Thus, there is a g...

Predicting cardiovascular disease in patients with mental illness using machine learning.

European psychiatry : the journal of the Association of European Psychiatrists
BACKGROUND: Cardiovascular disease (CVD) is twice as prevalent among individuals with mental illness compared to the general population. Prevention strategies exist but require accurate risk prediction. This study aimed to develop and validate a mach...

PRAF2 as a novel biomarker for breast cancer with machine learning and experimentation validation.

BMC cancer
BACKGROUND: Breast cancer (BC) is the most prevalent malignancy in women. Potential therapeutic targets for BC are of great significance. In our previous study, we found that prenylated rab acceptor 1 domain family member 2 (PRAF2) is an oncogene in ...

Machine learning-derived asthma and allergy trajectories in children: a systematic review and meta-analysis.

European respiratory review : an official journal of the European Respiratory Society
INTRODUCTION: Numerous studies have characterised trajectories of asthma and allergy in children using machine learning, but with different techniques and mixed findings. The present work aimed to summarise the evidence and critically appraise the me...

Classification of soil contamination by heavy metals (Cr, Ni, Pb, Zn) in wildfire-affected areas using laser-induced breakdown spectroscopy and machine learning.

Environmental science and pollution research international
The assessment of soil contamination by heavy metals is of high importance due to its impact on the environment and human health. Standard high-sensitivity spectroscopic techniques for this task such as atomic absorption spectrometry (AAS) and induct...

An efficient smart phone application for wheat crop diseases detection using advanced machine learning.

PloS one
Globally, agriculture holds significant importance for human food, economic activities, and employment opportunities. Wheat stands out as the most cultivated crop in the farming sector; however, its annual production faces considerable challenges fro...

Uncovering blood-brain barrier permeability: a comparative study of machine learning models using molecular fingerprints, and SHAP explainability.

SAR and QSAR in environmental research
This study illustrates the use of chemical fingerprints with machine learning for blood-brain barrier (BBB) permeability prediction. Employing the Blood Brain Barrier Database (B3DB) dataset for BBB permeability prediction, we extracted nine differen...

Comprehensive bioinformatics analysis identifies metabolic and immune-related diagnostic biomarkers shared between diabetes and COPD using multi-omics and machine learning.

Frontiers in endocrinology
BACKGROUND: Diabetes and chronic obstructive pulmonary disease (COPD) are prominent global health challenges, each imposing significant burdens on affected individuals, healthcare systems, and society. However, the specific molecular mechanisms suppo...

Leveraging AI to improve disease screening among American Indians: insights from the Strong Heart Study.

Experimental biology and medicine (Maywood, N.J.)
Screening tests for disease have their performance measured through sensitivity and specificity, which inform how well the test can discriminate between those with and without the condition. Typically, high values for sensitivity and specificity are ...