STUDY OBJECTIVE: Delirium is a common complication after cardiac surgery and is associated with poor prognosis. An effective delirium prediction model could identify high-risk patients who might benefit from targeted prevention strategies. We introdu...
BACKGROUND: Early diagnosis and accurate prognosis of cognitive decline in Alzheimer's disease (AD) is important to timely assignment to optimal treatment modes. We aimed to develop a deep learning model to predict cognitive conversion to guide re-as...
World journal of emergency surgery : WJES
Feb 13, 2025
BACKGROUND: Early treatment and prevention are the keys to reducing the mortality of VTE in patients with thoracic trauma. This study aimed to develop and validate an automatic prediction model based on machine learning for VTE risk screening in pati...
Low-density lipoprotein cholesterol (LDL-C) is an important factor in the development of cardiovascular disease, making its management a key aspect of cardiovascular health. While high-dose statin therapy is often recommended for LDL-C reduction, car...
BACKGROUND: The latest advancement of artificial intelligence (AI) is generative pretrained transformer large language models (LLMs). They have been trained on massive amounts of text, enabling humanlike and semantical responses to text-based inputs ...
Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Feb 13, 2025
PURPOSE: To develop models using different machine learning algorithms to predict high-risk symptom burden clusters in breast cancer patients undergoing chemotherapy, and to determine an optimal model.
Journal of clinical densitometry : the official journal of the International Society for Clinical Densitometry
Feb 13, 2025
INTRODUCTION: To investigate the accuracy of an artificial intelligence (AI) prototype in determining bone mineral density (BMD) in chronic obstructive pulmonary disease (COPD) patients using chest computed tomography (CT) scans.
PURPOSE: This study aimed to evaluate the efficacy of robot-assisted gait training (RAGT) in older patients with neurological gait disorder accompanied by various comorbidities.
PURPOSE: This study aims to explore the potential of non-contrast abdominal CT radiomics and deep learning models in accurately diagnosing fatty liver.
INTRODUCTION: The interpretation of plain hip radiographs can vary widely among physicians. This study aimed to develop and validate a deep learning-based screening model for distinguishing normal hips from severe hip diseases on plain radiographs.
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