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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.
Editorial. The risk of a pBIG miss with algorithmic care in pediatric traumatic brain injury.
J Neurosurg Pediatr
January 2025
2Division of Neurosurgery, Children's Hospital Los Angeles, California.
Advances in neurosurgical education: literature review of mixed-reality simulation models and novel mixed-reality spine prototype.
J Neurosurg Spine
January 2025
1Department of Neurosurgery, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, Arizona; and.
Objective: Mixed-reality (MR) applications provide opportunities for technical rehearsal, education, and estimation of surgical performance without the risk of patient harm. In this study, the authors provide a structured literature review on the current state of MR applications and their effects on neurosurgery training. They also introduce an MR prototype for neurosurgical spine training.
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