AIMC Topic: Machine Learning

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Development and Validation of Machine Learning-Based Race-Specific Models to Predict 10-Year Risk of Heart Failure: A Multicohort Analysis.

Circulation
BACKGROUND: Heart failure (HF) risk and the underlying risk factors vary by race. Traditional models for HF risk prediction treat race as a covariate in risk prediction and do not account for significant parameters such as cardiac biomarkers. Machine...

Predictive performance of machine and statistical learning methods: Impact of data-generating processes on external validity in the "large N, small p" setting.

Statistical methods in medical research
Machine learning approaches are increasingly suggested as tools to improve prediction of clinical outcomes. We aimed to identify when machine learning methods perform better than a classical learning method. We hereto examined the impact of the data-...

Statistical and Machine Learning Models for Classification of Human Wear and Delivery Days in Accelerometry Data.

Sensors (Basel, Switzerland)
Accelerometers are increasingly being used in biomedical research, but the analysis of accelerometry data is often complicated by both the massive size of the datasets and the collection of unwanted data from the process of delivery to study particip...

Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus.

Scientific reports
Bellflower is an edible ornamental gardening plant in Asia. For predicting the flower color in bellflower plants, a transcriptome-wide approach based on machine learning, transcriptome, and genotyping chip analyses was used to identify SNP markers. S...

An Explainable Artificial Intelligence Framework for the Deterioration Risk Prediction of Hepatitis Patients.

Journal of medical systems
In recent years, artificial intelligence-based computer aided diagnosis (CAD) system for the hepatitis has made great progress. Especially, the complex models such as deep learning achieve better performance than the simple ones due to the nonlinear ...

Machine Learning Algorithms for Predicting Fatty Liver Disease.

Annals of nutrition & metabolism
BACKGROUND: Fatty liver disease (FLD) has become a rampant condition. It is associated with a high rate of morbidity and mortality in a population. The condition is commonly referred as FLD. Early prediction of FLD would allow patients to take necess...

Current Status and Quality of Machine Learning-Based Radiomics Studies for Glioma Grading: A Systematic Review.

Oncology
INTRODUCTION: Radiomics now has significant momentum in the era of precision medicine. Glioma is one of the pathologies that has been extensively evaluated by radiomics. However, this technique has not been incorporated into clinical practice. In thi...

Current and emerging artificial intelligence applications for pediatric abdominal imaging.

Pediatric radiology
Artificial intelligence (AI) uses computers to mimic cognitive functions of the human brain, allowing inferences to be made from generally large datasets. Traditional machine learning (e.g., decision tree analysis, support vector machines) and deep l...