AIMC Topic: Machine Learning

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Probabilistic Contextual and Structural Dependencies Learning in Grammar-Based Genetic Programming.

Evolutionary computation
Genetic Programming is a method to automatically create computer programs based on the principles of evolution. The problem of deceptiveness caused by complex dependencies among components of programs is challenging. It is important because it can mi...

Machine learning and network medicine: a novel approach for precision medicine and personalized therapy in cardiomyopathies.

Journal of cardiovascular medicine (Hagerstown, Md.)
The early identification of pathogenic mechanisms is essential to predict the incidence and progression of cardiomyopathies and to plan appropriate preventive interventions. Noninvasive cardiac imaging such as cardiac computed tomography, cardiac mag...

Machine learning-based estimation of cognitive performance using regional brain MRI markers: the Northern Manhattan Study.

Brain imaging and behavior
High dimensional neuroimaging datasets and machine learning have been used to estimate and predict domain-specific cognition, but comparisons with simpler models composed of easy-to-measure variables are limited. Regularization methods in particular ...

State of machine and deep learning in histopathological applications in digestive diseases.

World journal of gastroenterology
Machine learning (ML)- and deep learning (DL)-based imaging modalities have exhibited the capacity to handle extremely high dimensional data for a number of computer vision tasks. While these approaches have been applied to numerous data types, this ...

Semi-supervised random forest regression model based on co-training and grouping with information entropy for evaluation of depression symptoms severity.

Mathematical biosciences and engineering : MBE
Semi-supervised learning has always been a hot topic in machine learning. It uses a large number of unlabeled data to improve the performance of the model. This paper combines the co-training strategy and random forest to propose a novel semi-supervi...

On the Efficiency of Machine Learning Models in Malaria Prediction.

Studies in health technology and informatics
Malaria is still a real public health concern in Sub-Saharan African countries such as Senegal where it represents approximately 35% of the consultation activities in the hospitals. This is mainly due to the lack of appropriate medical care support a...

Machine Learning Algorithms Reveals Country-Specific Metagenomic Taxa from American Gut Project Data.

Studies in health technology and informatics
In recent years, microbiota has become an increasingly relevant factor for the understanding and potential treatment of diseases. In this work, based on the data reported by the largest study of microbioma in the world, a classification model has bee...

Transfer Learning for Classifying Spanish and English Text by Clinical Specialties.

Studies in health technology and informatics
Transfer learning has demonstrated its potential in natural language processing tasks, where models have been pre-trained on large corpora and then tuned to specific tasks. We applied pre-trained transfer models to a Spanish biomedical document class...

Distributed Skin Lesion Analysis Across Decentralised Data Sources.

Studies in health technology and informatics
Skin cancer has become the most common cancer type. Research has applied image processing and analysis tools to support and improve the diagnose process. Conventional procedures usually centralise data from various data sources to a single location a...

Inter-Rater Reliability of Unstructured Text Labeling: Artificially vs. Naturally Intelligent Approaches.

Studies in health technology and informatics
Unstructured medical text labeling technologies are expected to be highly demanded since the interest in artificial intelligence and natural language processing arises in the medical domain. Our study aimed to assess the agreement between experts who...