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Construction and validation of a machine learning-based prediction model for short-term mortality in critically ill patients with liver cirrhosis.

Clinics and research in hepatology and gastroenterology
OBJECTIVE: Critically ill patients with liver cirrhosis generally have a poor prognosis due to complications such as multiple organ failure. This study aims to develop a machine learning-based prediction model to forecast short-term mortality in crit...

Machine learning is better than surgeons at assessing unicompartmental knee replacement radiographs.

The Knee
BACKGROUND: Poor results occasionally occur after unicompartmental knee replacement (UKR). It is often difficult, even for experienced surgeons, to determine why patients have poor outcomes from radiographs. The aim was to compare the ability of expe...

Integrating Metabolomics Domain Knowledge with Explainable Machine Learning in Atherosclerotic Cardiovascular Disease Classification.

International journal of molecular sciences
Metabolomic data often present challenges due to high dimensionality, collinearity, and variability in metabolite concentrations. Machine learning (ML) application in metabolomic analyses is enabling the extraction of meaningful information from comp...

Predicting cortical-thalamic functional connectivity using functional near-infrared spectroscopy and graph convolutional networks.

Scientific reports
Functional near-infrared spectroscopy (fNIRS) measures cortical hemodynamic changes, yet it cannot collect this information from subcortical structures, such as the thalamus, which is involved in several key functional networks. To address this drawb...

Predictive model for abdominal liposuction volume in patients with obesity using machine learning in a longitudinal multi-center study in Korea.

Scientific reports
This study aimed to develop and validate a machine learning (ML)-based model for predicting liposuction volumes in patients with obesity. This study used longitudinal cohort data from 2018 to 2023 from five nationwide centers affiliated with 365MC Li...

Impact of deep learning reconstruction on radiation dose reduction and cancer risk in CT examinations: a real-world clinical analysis.

European radiology
PURPOSE: The purpose of this study is to estimate the extent to which the implementation of deep learning reconstruction (DLR) may reduce the risk of radiation-induced cancer from CT examinations, utilizing real-world clinical data.

Development and Validation of a Machine Learning Radiomics Model based on Multiparametric MRI for Predicting Progesterone Receptor Expression in Meningioma: A Multicenter Study.

Academic radiology
RATIONALE AND OBJECTIVES: This study aimed to develop and validate a machine learning-based prediction model for preoperatively predicting progesterone receptor (PR) expression in meningioma patients using multiparametric magnetic resonance imaging (...

Attitudes of nurses toward artificial intelligence: A multicenter comparison.

Work (Reading, Mass.)
BackgroundArtificial intelligence (AI) is transforming medical practices with rapidly developing technologies and the innovative solutions it provides. In order for this transformation to be successfully integrated into healthcare services, healthcar...

Data - Knowledge driven machine learning model for cancer pain medication decisions.

International journal of medical informatics
BACKGROUND: Cancer pain is one of the most common symptoms in cancer patients, and drug decision-making in cancer pain management remains challenges. This study aims to develop machine learning models using real-world clinical data and prior knowledg...

A randomized controlled trial on evaluating clinician-supervised generative AI for decision support.

International journal of medical informatics
BACKGROUND: The integration of generative artificial intelligence (AI) as clinical decision support systems (CDSS) into telemedicine presents a significant opportunity to enhance clinical outcomes, yet its application remains underexplored.