AIMC Topic: Machine Learning

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Development and Validation of a Model Based on Circulating Biomarkers for Discriminating Symptomatic Spontaneous Intracranial Artery Dissection.

Translational stroke research
Spontaneous intracranial artery dissection (sIAD) is the leading cause of stroke in young individuals. Identifying high-risk sIAD cases that exhibit symptoms and are likely to progress is crucial for treatment decision-making. This study aimed to dev...

The Influence of Surface Treatment on the Color of Enamel and Dentin: An In Vitro Study Using Machine Learning-Based Analysis.

Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.]
OBJECTIVE: To investigate how surface treatment affects the color of enamel and dentin, and to evaluate whether the color differences are acceptable.

Machine learning models based on FEM simulation of hoop mode vibrations to enable ultrasonic cuffless measurement of blood pressure.

Medical & biological engineering & computing
Blood pressure (BP) is one of the vital physiological parameters, and its measurement is done routinely for almost all patients who visit hospitals. Cuffless BP measurement has been of great research interest over the last few years. In this paper, w...

[Population screening for acupuncture treatment of neck pain: a machine learning study].

Zhongguo zhen jiu = Chinese acupuncture & moxibustion
OBJECTIVE: To screen the population for acupuncture treatment of neck pain, using functional magnetic resonance imaging (fMRI) technology and based on machine learning algorithms.

A novel swarm budorcas taxicolor optimization-based multi-support vector method for transformer fault diagnosis.

Neural networks : the official journal of the International Neural Network Society
To address the challenge of low recognition accuracy in transformer fault detection, a novel method called swarm budorcas taxicolor optimization-based multi-support vector (SBTO-MSV) is proposed. Firstly, a multi-support vector (MSV) model is propose...

Uncertainty modeling for inductive knowledge graph embedding.

Neural networks : the official journal of the International Neural Network Society
In the process of refining Knowledge Graphs (KGs), new entities emerge, and old entities evolve, which usually updates their attribute information and neighborhood structures. This results in a distribution shift problem for entity features in the em...

Predicting and Ranking Diabetic Ketoacidosis Risk Among Youth with Type 1 Diabetes with a Clinic-to-Clinic Transferrable Machine Learning Model.

Diabetes technology & therapeutics
To use electronic health record (EHR) data to develop a scalable and transferrable model to predict 6-month risk for diabetic ketoacidosis (DKA)-related hospitalization or emergency care in youth with type 1 diabetes (T1D). To achieve a sharable pr...

Machine learning-based 28-day mortality prediction model for elderly neurocritically Ill patients.

Computer methods and programs in biomedicine
BACKGROUND: The growing population of elderly neurocritically ill patients highlights the need for effective prognosis prediction tools. This study aims to develop and validate machine learning (ML) models for predicting 28-day mortality in intensive...

Machine learning and statistical shape modelling for real-time prediction of stent deployment in realistic anatomies.

Computer methods and programs in biomedicine
The rise in minimally invasive procedures has created a demand for efficient and reliable planning software to predict intra- and post-operative outcomes. Surrogate modelling has shown promise, but challenges remain, particularly in cardiovascular ap...

Classification of α-thalassemia data using machine learning models.

Computer methods and programs in biomedicine
BACKGROUND: Around 7% of the global population has congenital hemoglobin disorders, with over 300,000 new cases of α-thalassemia annually. Diagnosis is costly and inaccurate in low-income regions, often relying on complete blood count (CBC) tests. Th...