Support that mitigates the detrimental impact of adverse events on human healthcare practitioners is underpinned by an understanding of their experiences. This study used a mixed methods approach to understand veterinary practitioners' responses to a...
American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons
Dec 4, 2024
The coronavirus disease 2019 pandemic has underscored the importance of vaccines, especially for immunocompromised populations like solid organ transplant recipients, who often have weaker immune responses. The purpose of this study was to compare de...
RATIONALE AND OBJECTIVES: To develop interpretable machine learning models that utilize deep learning (DL) and radiomics based on multiparametric Magnetic resonance imaging (MRI) to predict preoperative lymph node (LN) metastasis in rectal cancer.
BACKGROUND: Cardiac magnetic resonance (CMR) imaging is an important diagnostic tool for diagnosis of cardiac amyloidosis (CA). However, discrimination of CA from other etiologies of myocardial disease can be challenging.
Machine learning models for the diagnosis of breast cancer can facilitate the prediction of cancer risk and subsequent patient management among other clinical tasks. For the models to impact clinical practice, they ought to follow standard workflows,...
Circulation journal : official journal of the Japanese Circulation Society
Dec 4, 2024
BACKGROUND: The angiography-derived non-hyperemic pressure ratio (angioNHPR) is a novel index of NHPR based on artificial intelligence (AI) that does not require pressure wires. We investigated the diagnostic accuracy of angioNHPR for detecting hemod...
PURPOSE: Accurate and automated early survival prediction is critical for patients with glioblastoma (GBM) as their poor prognosis requires timely treatment decision-making. To address this need, we developed a deep learning (DL)-based end-to-end wor...
INTRODUCTION: In Parkinson's Disease (PD), despite available treatments focusing on symptom alleviation, the effectiveness of conventional therapies decreases over time. This study aims to enhance the identification of candidates for device-aided the...
Previous models of depression outcomes have been limited by symptom heterogeneity within populations. This study conducted a retrospective analysis using latent growth mixture models to identify heterogeneous trajectories within a clinical population...
OBJECTIVE: The capacity of machine-learning algorithms to predict medication adherence was assessed using data from AiCure, a computer vision-assisted smartphone application, which records the medication ingestion event.
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