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Development of an artificial intelligence-based application for the diagnosis of sarcopenia: a retrospective cohort study using the health examination dataset.
BMC medical informatics and decision making
Feb 5, 2025
BACKGROUND: Medical imaging techniques for diagnosing sarcopenia have been extensively investigated. Studies have proposed using the T-score and patient information as key diagnostic factors. However, these techniques have either been time-consuming ...
Ensemble learning to predict short birth interval among reproductive-age women in Ethiopia: evidence from EDHS 2016-2019.
BMC pregnancy and childbirth
Feb 5, 2025
BACKGROUND: A birth interval of less than 33 months was considered short, and in low- income countries like Ethiopia, a short birth interval is the primary cause of approximately 822 maternal deaths every day. Due to that this study aimed to predict ...
Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements.
Scientific reports
Feb 5, 2025
Chronic kidney disease (CKD) is a global health concern with early detection playing a pivotal role in effective management. Machine learning models demonstrate promise in CKD detection, yet the impact on detection and classification using different ...
Constructing a machine learning model for systemic infection after kidney stone surgery based on CT values.
Scientific reports
Feb 5, 2025
This study aims to develop a machine learning model utilizing Computed Tomography (CT) values to predict systemic inflammatory response syndrome (SIRS) after endoscopic surgery for kidney stones. The goal is to identify high-risk patients early and p...
Identification of Clusters in a Population With Obesity Using Machine Learning: Secondary Analysis of The Maastricht Study.
JMIR medical informatics
Feb 5, 2025
BACKGROUND: Modern lifestyle risk factors, like physical inactivity and poor nutrition, contribute to rising rates of obesity and chronic diseases like type 2 diabetes and heart disease. Particularly personalized interventions have been shown to be e...
A Fully Automated Artificial Intelligence-Based Approach to Predict Renal Function After Radical or Partial Nephrectomy.
Urology
Feb 4, 2025
OBJECTIVE: To test if our artificial intelligence (AI)-postoperative glomerular filtration rate (GFR) prediction is as accurate as a validated clinical model. The American Urologic Association recommends estimating postoperative GFR in patients with ...
Emotional stimulated speech-based assisted early diagnosis of depressive disorders using personality-enhanced deep learning.
Journal of affective disorders
Feb 4, 2025
BACKGROUND: Early diagnosis of depression is crucial, and speech-based early diagnosis of depression is promising, but insufficient data and lack of theoretical support make it difficult to be applied. Therefore, it is valuable to combine psychiatric...
Development and usability evaluation of a nurse-led clinical decision support system (AI-AntiDelirium) for management of intensive care unit delirium: A mixed methods study.
Applied nursing research : ANR
Feb 4, 2025
BACKGROUND: Clinical decision support systems (CDSS) have been identified to aid clinical decision-making, but few studies focus on the application of CDSS in intensive care unit (ICU) delirium, and particularly usability testing is not employed. We ...
Machine Learning-based World Health Organization Disability Assessment Schedule for persons with Parkinson's disease.
Parkinsonism & related disorders
Feb 4, 2025
INTRODUCTION: The World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) is a well-known measure to assess disability in persons with Parkinson's disease (PD). The purpose of this study was to develop a short form of the WHODAS 2.0...
Personalized auto-segmentation for magnetic resonance imaging-guided adaptive radiotherapy of large brain metastases.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Feb 4, 2025
BACKGROUND AND PURPOSE: Magnetic resonance-guided adaptive radiotherapy (MRgART) may improve the efficacy of large brain metastases (BMs)(≥2 cm), whereas the workflow requires optimized. This study develops a two-stage, personalized deep learning aut...
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