AIMC Topic: Risk Assessment

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Predictors of left atrial appendage thrombus in atrial fibrillation patients undergoing cardioversion.

Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing
BACKGROUND: Atrial fibrillation and atrial flutter represent the most prevalent clinically significant cardiac arrhythmias. While the CHA2DS2-VASc score is commonly used to inform anticoagulation therapy decisions for patients with these conditions, ...

Smart Cushions with Machine Learning-Enhanced Force Sensors for Pressure Injury Risk Assessment.

ACS applied materials & interfaces
Prolonged sitting can easily result in pressure injury (PI) for certain people who have had strokes or spinal cord injuries. There are not many methods available for tracking contact surface pressure and shear force to evaluate the PI risk. Here, we ...

Interpretable machine learning for the prediction of death risk in patients with acute diquat poisoning.

Scientific reports
The aim of this study was to develop and validate predictive models for assessing the risk of death in patients with acute diquat (DQ) poisoning using innovative machine learning techniques. Additionally, predictive models were evaluated through the ...

Machine learning vs. regression models to predict the risk of Legionella contamination in a hospital water network.

Annali di igiene : medicina preventiva e di comunita
INTRODUCTION: The periodic monitoring of Legionella in hospital water networks allows preventive measures to be taken to avoid the risk of legionellosis to patients and healthcare workers.

A novel approach for the effective prediction of cardiovascular disease using applied artificial intelligence techniques.

ESC heart failure
AIMS: The objective of this research is to develop an effective cardiovascular disease prediction framework using machine learning techniques and to achieve high accuracy for the prediction of cardiovascular disease.

Uveal melanoma distant metastasis prediction system: A retrospective observational study based on machine learning.

Cancer science
Uveal melanoma (UM) patients face a significant risk of distant metastasis, closely tied to a poor prognosis. Despite this, there is a dearth of research utilizing big data to predict UM distant metastasis. This study leveraged machine learning metho...

Artificial intelligence-enhanced opportunistic screening of osteoporosis in CT scan: a scoping Review.

Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA
PURPOSE: This scoping review aimed to assess the current research on artificial intelligence (AI)--enhanced opportunistic screening approaches for stratifying osteoporosis and osteopenia risk by evaluating vertebral trabecular bone structure in CT sc...

Machine learning-based autophagy-related prognostic signature for personalized risk stratification and therapeutic approaches in bladder cancer.

International immunopharmacology
OBJECTIVE: Bladder cancer (BCa) is a highly lethal urological malignancy characterized by its notable histological heterogeneity. Autophagy has swiftly emerged as a diagnostic and prognostic biomarker in diverse cancer types. Nonetheless, the current...

Deep learning for predicting rehospitalization in acute heart failure: Model foundation and external validation.

ESC heart failure
AIMS: Assessing the risk for HF rehospitalization is important for managing and treating patients with HF. To address this need, various risk prediction models have been developed. However, none of them used deep learning methods with real-world data...

Evaluation of fragility fracture risk using deep learning based on ultrasound radio frequency signal.

Endocrine
BACKGROUND: It was essential to identify individuals at high risk of fragility fracture and prevented them due to the significant morbidity, mortality, and economic burden associated with fragility fracture. The quantitative ultrasound (QUS) showed p...