BACKGROUND: Intracerebral amyloid β (Aβ) accumulation is considered the initial observable event in the pathological process of Alzheimer's disease (AD). Efficient screening for amyloid pathology is critical for identifying patients for early treatme...
OBJECTIVES: To develop a machine learning-based prediction model using clinical data from the first 24 h of ICU admission to enable rapid screening and early intervention for sepsis patients.
INTRODUCTION: Diabetic nephropathy (DN) is a common diabetes-related complication with unclear underlying pathological mechanisms. Although recent studies have linked glycolysis to various pathological states, its role in DN remains largely underexpl...
BMC medical informatics and decision making
Jan 21, 2025
This study aimed to compare and evaluate the prediction accuracy and risk of bias (ROB) of post-traumatic stress disorder (PTSD) predictive models. We conducted a systematic review and random-effect meta-analysis summarizing predictive model developm...
Climate change and human activities affect the biomass of different algal and the succession of dominant species. In the past, phytoplankton phyla inversion has been focused on oceanic and continental shelf waters, while phytoplankton phyla inversion...
Machine learning models are vital for forecasting and optimizing healthcare parameters, especially in the context of rising mental health issues in India and globally. With increasing demand for mental health services, effective resource management, ...
The hyoid bone has been identified as sexually dimorphic in various populations. The current study is a forerunner analysis that used three-dimensional multidetector computed tomography (3D MDCT) images of the hyoid bone to examine sexual dimorphism ...
Patients with Diabetic ketoacidosis (DKA) have increased critical illness and mortality during coronavirus diseases 2019 (COVID-19). The aim of our study was to develop a predictive model for the occurrence of critical illness and mortality in COVID-...
Existing dementia prediction models using non-neuroimaging clinical measures have been limited in their ability to identify disease. This study used machine learning to re-examine the diagnostic potential of clinical measures for dementia. Data was s...
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