AIMC Topic: Models, Theoretical

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A pre-training and self-training approach for biomedical named entity recognition.

PloS one
Named entity recognition (NER) is a key component of many scientific literature mining tasks, such as information retrieval, information extraction, and question answering; however, many modern approaches require large amounts of labeled training dat...

Deep action learning enables robust 3D segmentation of body organs in various CT and MRI images.

Scientific reports
In this study, we propose a novel point cloud based 3D registration and segmentation framework using reinforcement learning. An artificial agent, implemented as a distinct actor based on value networks, is trained to predict the optimal piece-wise li...

Voting-based integration algorithm improves causal network learning from interventional and observational data: An application to cell signaling network inference.

PloS one
In order to increase statistical power for learning a causal network, data are often pooled from multiple observational and interventional experiments. However, if the direct effects of interventions are uncertain, multi-experiment data pooling can r...

Machine learning predictive model for severe COVID-19.

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases
To develop a modified predictive model for severe COVID-19 in people infected with Sars-Cov-2. We developed the predictive model for severe patients of COVID-19 based on the clinical date from the Tumor Center of Union Hospital affiliated with Tongji...

Machine learning for buildings' characterization and power-law recovery of urban metrics.

PloS one
In this paper we focus on a critical component of the city: its building stock, which holds much of its socio-economic activities. In our case, the lack of a comprehensive database about their features and its limitation to a surveyed subset lead us ...

Towards a Precision Medicine Approach Based on Machine Learning for Tailoring Medical Treatment in Alkaptonuria.

International journal of molecular sciences
ApreciseKUre is a multi-purpose digital platform facilitating data collection, integration and analysis for patients affected by Alkaptonuria (AKU), an ultra-rare autosomal recessive genetic disease. It includes genetic, biochemical, histopathologica...

Contributions and limitations of using machine learning to predict noise-induced hearing loss.

International archives of occupational and environmental health
PURPOSE: Noise-induced hearing loss (NIHL) is a global issue that impacts people's life and health. The current review aims to clarify the contributions and limitations of applying machine learning (ML) to predict NIHL by analyzing the performance of...

A dynamic generalized fuzzy multi-criteria croup decision making approach for green supplier segmentation.

PloS one
Supplier selection and segmentation are crucial tasks of companies in order to reduce costs and increase the competitiveness of their goods. To handle uncertainty and dynamicity in the supplier segmentation problem, this research thus proposes a new ...

Deep Learning Approach to Parse Eligibility Criteria in Dietary Supplements Clinical Trials Following OMOP Common Data Model.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Dietary supplements (DSs) have been widely used in the U.S. and evaluated in clinical trials as potential interventions for various diseases. However, many clinical trials face challenges in recruiting enough eligible patients in a timely fashion, ca...

Applying machine learning, text mining, and spatial analysis techniques to develop a highway-railroad grade crossing consolidation model.

Accident; analysis and prevention
The consolidation of Highway-Railroad Grade Crossing (HRGC) is one of the effective approaches to decrease the number of crashes between trains and vehicles. From 2015-2019, there were 57 HRGC crashes at crossings in East Baton Rouge Parish (EBRP), r...