AIMC Topic: Machine Learning

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Machine Learning in Cardiac Imaging: Exploring the Art of Cluster Analysis.

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography

ET-score: Improving Protein-ligand Binding Affinity Prediction Based on Distance-weighted Interatomic Contact Features Using Extremely Randomized Trees Algorithm.

Molecular informatics
The molecular docking simulation is a key computational tool in modern drug discovery research that its predictive performance strongly depends on the employed scoring functions. Many recent studies have shown that the application of machine learning...

Fast screening of covariates in population models empowered by machine learning.

Journal of pharmacokinetics and pharmacodynamics
One of the objectives of Pharmacometry (PMX) population modeling is the identification of significant and clinically relevant relationships between parameters and covariates. Here, we demonstrate how this complex selection task could benefit from sup...

An interpretable machine learning prognostic system for locoregionally advanced nasopharyngeal carcinoma based on tumor burden features.

Oral oncology
OBJECTIVES: We aimed to build a survival system by combining a highly-accurate machine learning (ML) model with explainable artificial intelligence (AI) techniques to predict distant metastasis in locoregionally advanced nasopharyngeal carcinoma (NPC...

Domain Generalization in Biosignal Classification.

IEEE transactions on bio-medical engineering
OBJECTIVE: When training machine learning models, we often assume that the training data and evaluation data are sampled from the same distribution. However, this assumption is violated when the model is evaluated on another unseen but similar databa...

Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning.

IEEE transactions on bio-medical engineering
BACKGROUND: Despite recent significant progress in the development of automatic sleep staging methods, building a good model still remains a big challenge for sleep studies with a small cohort due to the data-variability and data-inefficiency issues....

Permutation-based identification of important biomarkers for complex diseases via machine learning models.

Nature communications
Study of human disease remains challenging due to convoluted disease etiologies and complex molecular mechanisms at genetic, genomic, and proteomic levels. Many machine learning-based methods have been developed and widely used to alleviate some anal...

Generalisability through local validation: overcoming barriers due to data disparity in healthcare.

BMC ophthalmology
Cho et al. report deep learning model accuracy for tilted myopic disc detection in a South Korean population. Here we explore the importance of generalisability of machine learning (ML) in healthcare, and we emphasise that recurrent underrepresentati...

A Review of Applications of Machine Learning in Mammography and Future Challenges.

Oncology
BACKGROUND: The aim of this study is to systematically review the literature to summarize the evidence surrounding the clinical utility of artificial intelligence (AI) in the field of mammography. Databases from PubMed, IEEE Xplore, and Scopus were s...

Interpreting weights of multimodal machine learning models-problems and pitfalls.

Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology