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Detecting Artificial Intelligence-Generated Versus Human-Written Medical Student Essays: Semirandomized Controlled Study.
JMIR medical education
Mar 3, 2025
BACKGROUND: Large language models, exemplified by ChatGPT, have reached a level of sophistication that makes distinguishing between human- and artificial intelligence (AI)-generated texts increasingly challenging. This has raised concerns in academia...
Development and Feasibility Study of HOPE Model for Prediction of Depression Among Older Adults Using Wi-Fi-based Motion Sensor Data: Machine Learning Study.
JMIR aging
Mar 3, 2025
BACKGROUND: Depression, characterized by persistent sadness and loss of interest in daily activities, greatly reduces quality of life. Early detection is vital for effective treatment and intervention. While many studies use wearable devices to class...
Applying machine learning to high-dimensional proteomics datasets for the identification of Alzheimer's disease biomarkers.
Fluids and barriers of the CNS
Mar 3, 2025
PURPOSE: This study explores the application of machine learning to high-dimensional proteomics datasets for identifying Alzheimer's disease (AD) biomarkers. AD, a neurodegenerative disorder affecting millions worldwide, necessitates early and accura...
Machine learning for the rElapse risk eValuation in acute biliary pancreatitis: The deep learning MINERVA study protocol.
World journal of emergency surgery : WJES
Mar 3, 2025
BACKGROUND: Mild acute biliary pancreatitis (MABP) presents significant clinical and economic challenges due to its potential for relapse. Current guidelines advocate for early cholecystectomy (EC) during the same hospital admission to prevent recurr...
Leveraging machine learning for duration of surgery prediction in knee and hip arthroplasty - a development and validation study.
BMC medical informatics and decision making
Mar 3, 2025
BACKGROUND: Duration of surgery (DOS) varies substantially for patients with hip and knee arthroplasty (HA/KA) and is a major risk factor for adverse events. We therefore aimed (1) to identify whether machine learning can predict DOS in HA/KA patient...
An interpretable machine learning model with demographic variables and dietary patterns for ASCVD identification: from U.S. NHANES 1999-2018.
BMC medical informatics and decision making
Mar 3, 2025
Current research on the association between demographic variables and dietary patterns with atherosclerotic cardiovascular disease (ASCVD) is limited in breadth and depth. This study aimed to construct a machine learning (ML) algorithm that can accur...
Utilization of non-invasive ventilation before prehospital emergency anesthesia in trauma - a cohort analysis with machine learning.
Scandinavian journal of trauma, resuscitation and emergency medicine
Mar 3, 2025
BACKGROUND: For preoxygenation, German guidelines consider non-invasive ventilation (NIV) as a possible method in prehospital trauma care in the absence of aspiration, severe head or face injuries, unconsciousness, or patient non-compliance. As data ...
Reliability and validity of a novel single-lead portable electrocardiogram device for pregnant women: a comparative study.
BMC medical informatics and decision making
Mar 3, 2025
BACKGROUND: WenXinWuYang, a novel portable Artificial Intelligence Electrocardiogram (AI-ECG) device, can detect many kinds of abnormal heart disease and perform a single-lead ECG, but its reliability and validity among pregnant women is unclear. The...
The role of senescence-related genes in major depressive disorder: insights from machine learning and single cell analysis.
BMC psychiatry
Mar 3, 2025
BACKGROUND: Evidence indicates that patients with Major Depressive Disorder (MDD) exhibit a senescence phenotype or an increased susceptibility to premature senescence. However, the relationship between senescence-related genes (SRGs) and MDD remains...
Development and validation of a deep learning algorithm for prediction of pediatric recurrent intussusception in ultrasound images and radiographs.
BMC medical imaging
Mar 3, 2025
PURPOSES: To develop a predictive model for recurrent intussusception based on abdominal ultrasound (US) images and abdominal radiographs.
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