AIMC Topic: Machine Learning

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Deep learning in random neural fields: Numerical experiments via neural tangent kernel.

Neural networks : the official journal of the International Neural Network Society
A biological neural network in the cortex forms a neural field. Neurons in the field have their own receptive fields, and connection weights between two neurons are random but highly correlated when they are in close proximity in receptive fields. In...

Online sequential, outlier robust, and parallel layer perceptron extreme learning machine models for sediment transport in sewer pipes.

Environmental science and pollution research international
Sediment transport is a noteworthy task in the design and operation of sewer pipes. Decreasing sewer pipe hydraulic capacity and transport of pollution are the main consequences of continuous sedimentation. Among different design approaches, the non-...

Radiomic-based machine learning model for the accurate prediction of prostate cancer risk stratification.

The British journal of radiology
OBJECTIVES: To precisely predict prostate cancer (PCa) risk stratification, we constructed a machine learning (ML) model based on magnetic resonance imaging (MRI) radiomic features.

Negation-based transfer learning for improving biomedical Named Entity Recognition and Relation Extraction.

Journal of biomedical informatics
BACKGROUND AND OBJECTIVES: Named Entity Recognition (NER) and Relation Extraction (RE) are two of the most studied tasks in biomedical Natural Language Processing (NLP). The detection of specific terms and entities and the relationships between them ...

Machine learning concepts applied to oral pathology and oral medicine: A convolutional neural networks' approach.

Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology
INTRODUCTION: Artificial intelligence models and networks can learn and process dense information in a short time, leading to an efficient, objective, and accurate clinical and histopathological analysis, which can be useful to improve treatment moda...

Improving clinical trial design using interpretable machine learning based prediction of early trial termination.

Scientific reports
This study proposes using a machine learning pipeline to optimise clinical trial design. The goal is to predict early termination probability of clinical trials using machine learning modelling, and to understand feature contributions driving early t...

A Novel Tropical Geometry-Based Interpretable Machine Learning Method: Pilot Application to Delivery of Advanced Heart Failure Therapies.

IEEE journal of biomedical and health informatics
A model's interpretability is essential to many practical applications such as clinical decision support systems. In this article, a novel interpretable machine learning method is presented, which can model the relationship between input variables an...

Sleep Classification With Artificial Synthetic Imaging Data Using Convolutional Neural Networks.

IEEE journal of biomedical and health informatics
OBJECTIVE: We propose a new analytic framework, "Artificial Synthetic Imaging Data (ASID) Workflow," for sleep classification from a wearable device comprising: 1) the creation of ASID from data collected by a non-invasive wearable device that permit...

Exploratory pharmacovigilance with machine learning in big patient data: A focused scoping review.

Basic & clinical pharmacology & toxicology
BACKGROUND: Machine learning can operationalize the rich and complex data in electronic patient records for exploratory pharmacovigilance endeavours.

Artificial intelligence and machine learning approaches in composting process: A review.

Bioresource technology
Studies on developing strategies to predict the stability and performance of the composting process have increased in recent years. Machine learning (ML) has focused on process optimization, prediction of missing data, detection of non-conformities, ...