AIMC Topic: Machine Learning

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Machine learning model predicts airway stenosis requiring clinical intervention in patients after lung transplantation: a retrospective case-controlled study.

BMC medical informatics and decision making
BACKGROUND: Patients with airway stenosis (AS) are associated with considerable morbidity and mortality after lung transplantation (LTx). This study aims to develop and validate machine learning (ML) models to predict AS requiring clinical interventi...

Enhancing COVID-19 forecasting precision through the integration of compartmental models, machine learning and variants.

Scientific reports
Predicting epidemic evolution is essential for making informed decisions and guiding the implementation of necessary countermeasures. Computational models are vital tools that provide insights into illness progression and enable early detection, proa...

Predicting the risk category of thymoma with machine learning-based computed tomography radiomics signatures and their between-imaging phase differences.

Scientific reports
The aim of this study was to develop a medical imaging and comprehensive stacked learning-based method for predicting high- and low-risk thymoma. A total of 126 patients with thymomas and 5 patients with thymic carcinoma treated at our institution, i...

A review on advancements in feature selection and feature extraction for high-dimensional NGS data analysis.

Functional & integrative genomics
Recent advancements in biomedical technologies and the proliferation of high-dimensional Next Generation Sequencing (NGS) datasets have led to significant growth in the bulk and density of data. The NGS high-dimensional data, characterized by a large...

Estimation of 100 m root zone soil moisture by downscaling 1 km soil water index with machine learning and multiple geodata.

Environmental monitoring and assessment
Root zone soil moisture (RZSM) is crucial for agricultural water management and land surface processes. The 1 km soil water index (SWI) dataset from Copernicus Global Land services, with eight fixed characteristic time lengths (T), requires root zone...

Using algorithmic game theory to improve supervised machine learning: A novel applicability approach in flood susceptibility mapping.

Environmental science and pollution research international
This study was carried out with the aim of applying Condorcet and Borda scoring algorithms based on Game Theory (GT) to determine flood points and Flood Susceptibility Mapping (FSM) based on Machine Learning Algorithms (MLA) including Random Forest (...

Applying 12 machine learning algorithms and Non-negative Matrix Factorization for robust prediction of lupus nephritis.

Frontiers in immunology
Lupus nephritis (LN) is a challenging condition with limited diagnostic and treatment options. In this study, we applied 12 distinct machine learning algorithms along with Non-negative Matrix Factorization (NMF) to analyze single-cell datasets from k...

A prognostic model for thermal ablation of benign thyroid nodules based on interpretable machine learning.

Frontiers in endocrinology
INTRODUCTION: The detection rate of benign thyroid nodules is increasing every year, with some affected patients experiencing symptoms. Ultrasound-guided thermal ablation can reduce the volume of nodules to alleviate symptoms. As the degree and speed...

Machine-learning models for shoulder rehabilitation exercises classification using a wearable system.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
PURPOSE: The objective of this study is to train and test machine-learning (ML) models to automatically classify shoulder rehabilitation exercises.

Artificial intelligence and prescription of antibiotic therapy: present and future.

Expert review of anti-infective therapy
INTRODUCTION: In the past few years, the use of artificial intelligence in healthcare has grown exponentially. Prescription of antibiotics is not exempt from its rapid diffusion, and various machine learning (ML) techniques, from logistic regression ...