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Major Adverse Kidney Events in Hospitalized Older Patients With Acute Kidney Injury: Machine Learning-Based Model Development and Validation Study.
J Med Internet Res
January 2025
Department of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, Changsha, China.
Background: Acute kidney injury (AKI) is a common complication in hospitalized older patients, associated with increased morbidity, mortality, and health care costs. Major adverse kidney events within 30 days (MAKE30), a composite of death, new renal replacement therapy, or persistent renal dysfunction, has been recommended as a patient-centered endpoint for clinical trials involving AKI.
Objective: This study aimed to develop and validate a machine learning-based model to predict MAKE30 in hospitalized older patients with AKI.
Development and Evaluation of a Mental Health Chatbot Using ChatGPT 4.0: Mixed Methods User Experience Study With Korean Users.
JMIR Med Inform
January 2025
Sungkyunkwan University, Seoul, Republic of Korea.
Background: Mental health chatbots have emerged as a promising tool for providing accessible and convenient support to individuals in need. Building on our previous research on digital interventions for loneliness and depression among Korean college students, this study addresses the limitations identified and explores more advanced artificial intelligence-driven solutions.
Objective: This study aimed to develop and evaluate the performance of HoMemeTown Dr.
Explainable Machine Learning to Predict Treatment Response in Advanced Non-Small Cell Lung Cancer.
JCO Clin Cancer Inform
January 2025
Emory University School of Medicine, Atlanta, GA.
Purpose: Immune checkpoint inhibitors (ICIs) have demonstrated promise in the treatment of various cancers. Single-drug ICI therapy (immuno-oncology [IO] monotherapy) that targets PD-L1 is the standard of care in patients with advanced non-small cell lung cancer (NSCLC) with PD-L1 expression ≥50%. We sought to find out if a machine learning (ML) algorithm can perform better as a predictive biomarker than PD-L1 alone.
View Article and Find Full Text PDFObject detection in motion management scenarios based on deep learning.
PLoS One
January 2025
School of Physical Education, Jinjiang College, Sichuan University, Chengdu, Sichuan Province, People's Republic of China.
In athletes' competitions and daily training, in order to further strengthen the athletes' sports level, it is usually necessary to analyze the athletes' sports actions at a specific moment, in which it is especially important to quickly and accurately identify the categories and positions of the athletes, sports equipment, field boundaries and other targets in the sports scene. However, the existing detection methods failed to achieve better detection results, and the analysis found that the reasons for this phenomenon mainly lie in the loss of temporal information, multi-targeting, target overlap, and coupling of regression and classification tasks, which makes it more difficult for these network models to adapt to the detection task in this scenario. Based on this, we propose for the first time a supervised object detection method for scenarios in the field of motion management.
View Article and Find Full Text PDFInvestigation of alpha-glucosidase inhibition activity of Artabotrys sumatranus leaf extract using metabolomics, machine learning and molecular docking analysis.
PLoS One
January 2025
Research Centre for Plant Conservation, Botanic Gardens and Forestry, National Research and Innovation Agency, Bogor, Indonesia.
One way to treat diabetes mellitus type II is by using α-glucosidase inhibitor, that will slow down the postprandial glucose intake. Metabolomics analysis of Artabotrys sumatranus leaf extract was used in this research to predict the active compounds as α-glucosidase inhibitors from this extract. Both multivariate statistical analysis and machine learning approaches were used to improve the confidence of the predictions.
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