AIMC Topic: Middle Aged

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Machine learning approach for the prediction of 30-day mortality in patients with sepsis-associated delirium.

PloS one
This study aimed to develop models for predicting the 30-day mortality of sepsis-associated delirium (SAD) by multiple machine learning (ML) algorithms. On the whole, a cohort of 3,197 SAD patients were collected from the Medical Information Mart for...

Diagnostic performance of attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy for detecting osteoarthritis and rheumatoid arthritis from blood serum.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Osteoarthritis (OA) and rheumatoid arthritis (RA) are the two most common rheumatic diseases worldwide, causing pain and disability. Both conditions are highly heterogeneous, and their onset occurs insidiously with non-specific symptoms, so they are ...

A machine learning approach for type 2 diabetes diagnosis and prognosis using tailored heterogeneous feature subsets.

Medical & biological engineering & computing
Type 2 diabetes (T2D) is becoming one of the leading health problems in Western societies, diminishing quality of life and consuming a significant share of healthcare resources. This study presents machine learning models for T2D diagnosis and progno...

Cross-institutional validation of a polar map-free 3D deep learning model for obstructive coronary artery disease prediction using myocardial perfusion imaging: insights into generalizability and bias.

European journal of nuclear medicine and molecular imaging
PURPOSE: Deep learning (DL) models for predicting obstructive coronary artery disease (CAD) using myocardial perfusion imaging (MPI) have shown potential for enhancing diagnostic accuracy. However, their ability to maintain consistent performance acr...

Development and validation of Prediction models for radiation-induced hypoglossal neuropathy in patients with nasopharyngeal carcinoma.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: To establish predictive models for radiation-induced hypoglossal neuropathy (RIHN) in patients with nasopharyngeal carcinoma (NPC) after intensity-modulated radiotherapy (IMRT).

Application of an interpretable machine learning method to predict the risk of death during hospitalization in patients with acute myocardial infarction combined with diabetes mellitus.

Acta cardiologica
BACKGROUND: Predicting the prognosis of patients with acute myocardial infarction (AMI) combined with diabetes mellitus (DM) is crucial due to high in-hospital mortality rates. This study aims to develop and validate a mortality risk prediction model...

The impact of digital competence on pedagogical innovation among nurse educators: The moderating role of artificial intelligence readiness.

Nurse education in practice
AIM: To investigate the relationships between digital competence, AI readiness and pedagogical innovation among nurse educators, with a specific focus on the moderating role of AI readiness.

Application of machine learning in assessing disease activity in SLE.

Lupus science & medicine
OBJECTIVE: SLE is a chronic autoimmune disease with immune complex deposition in various organs, causing inflammation. The Systemic Lupus Erythematosus Disease Activity Index 2000 assesses disease severity but is subjective. This study aimed to const...

An artificial intelligence model for predicting an appropriate mAs with target exposure indicator for chest digital radiography.

Scientific reports
In digital radiography, image quality is synergistically affected by anatomy-specific examinations, exposure factors, body parameters, detector types, and vendors/systems. However, estimating appropriate exposure factors before radiography with optim...

Machine learning integration of multimodal data identifies key features of circulating NT-proBNP in people without cardiovascular diseases.

Scientific reports
N-Terminal Pro-Brain Natriuretic Peptide (NT-proBNP) is important for diagnosing and predicting heart failure or many other diseases. However, few studies have comprehensively assessed the factors correlated with NT-proBNP levels in people with cardi...