AI Medical Compendium Topic

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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...

Natural language processing was effective in assisting rapid title and abstract screening when updating systematic reviews.

Journal of clinical epidemiology
BACKGROUND AND OBJECTIVE: To examine whether the use of natural language processing (NLP) technology is effective in assisting rapid title and abstract screening when updating a systematic review.

Artificial immune system features added to breast cancer clinical data for machine learning (ML) applications.

Bio Systems
We here propose a new method of combining a mathematical model that describes a chemotherapy treatment for breast cancer with a machine-learning (ML) algorithm to increase performance in predicting tumor size using a five-step procedure. The first st...

A comparison of the value of two machine learning predictive models to support bovine tuberculosis disease control in England.

Preventive veterinary medicine
Nearly a decade into Defra's current eradication strategy, bovine tuberculosis (bTB) remains a serious animal health problem in England, with c.30,000 cattle slaughtered annually in the fight against this insidious disease. There is an urgent need to...

Modelization of Covid-19 pandemic spreading: A machine learning forecasting with relaxation scenarios of countermeasures.

Journal of infection and public health
BACKGROUND & OBJECTIVE: Mathematical modeling is the most scientific technique to understand the evolution of natural phenomena, including the spread of infectious diseases. Therefore, these modeling tools have been widely used in epidemiology for pr...

In silico prediction of chemical acute contact toxicity on honey bees via machine learning methods.

Toxicology in vitro : an international journal published in association with BIBRA
In recent years, the decline of honey bees and the collapse of bee colonies have caught the attention of ecologists, and the use of pesticides is one of the main reasons for the decline. Therefore, ecological risk assessment of pesticides is essentia...