AIMC Topic: Early Diagnosis
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Development and application of an early prediction model for risk of bloodstream infection based on real-world study.
BMC medical informatics and decision making
May 14, 2025
BACKGROUND: Bloodstream Infection (BSI) is a severe systemic infectious disease that can lead to sepsis and Multiple Organ Dysfunction Syndrome (MODS), resulting in high mortality rates and posing a major public health burden globally. Early identifi...
Development and validation of an early diagnosis model for severe mycoplasma pneumonia in children based on interpretable machine learning.
Respiratory research
May 13, 2025
BACKGROUND: Pneumonia is a major threat to the health of children, especially those under the age of five. Mycoplasma pneumoniae infection is a core cause of pediatric pneumonia, and the incidence of severe mycoplasma pneumoniae pneumonia (SMPP) has...
Early detection of mental health disorders using machine learning models using behavioral and voice data analysis.
Scientific reports
May 13, 2025
People of all demographics are impacted by mental illness, which has become a widespread and international health problem. Effective treatment and support for mental illnesses depend on early discovery and precise diagnosis. Notably, delayed diagnosi...
Role of artificial intelligence in early identification and risk evaluation of non-communicable diseases: a bibliometric analysis of global research trends.
BMJ open
May 2, 2025
OBJECTIVE: This study aims to shed light on the transformative potential of artificial intelligence (AI) in the early detection and risk assessment of non-communicable diseases (NCDs).
Early detection of Alzheimer's disease using deep learning methods.
Alzheimer's & dementia : the journal of the Alzheimer's Association
May 1, 2025
INTRODUCTION: Alzheimer's disease (AD), a leading cause of dementia, requires early detection for effective intervention. This study employs AI to analyze multimodal datasets, including clinical, biomarker, and neuroimaging data, using hybrid deep le...
Predictive analytics for early detection of hospital-acquired complications: An artificial intelligence approach.
Health information management : journal of the Health Information Management Association of Australia
May 1, 2025
BACKGROUND: Hospital-acquired complications (HACs) have an adverse impact on patient recovery by impeding their path to full recovery and increasing healthcare costs.
Humans
Middle Aged
Cross Infection
Artificial Intelligence
Retrospective Studies
Risk Assessment
Machine Learning
Logistic Models
Western Australia
Intensive Care Units
Severity of Illness Index
Operative Time
Iatrogenic Disease
Length of Stay
Decision Trees
Aged, 80 and over
Predictive Learning Models
Early Diagnosis
Adult
Male
Female
Aged
Dynamic and Static Structure-Function Coupling With Machine Learning for the Early Detection of Alzheimer's Disease.
Human brain mapping
Apr 1, 2025
The progression of Alzheimer's disease (AD) involves complex changes in brain structure and function that are driven by their interaction, making structure-function coupling (SFC) a valuable indicator for early detection of AD. Static SFC refers to t...
Virtual reality and artificial intelligence: the future of mental health. A narrative review.
Recenti progressi in medicina
Mar 1, 2025
In recent years, the use of artificial intelligence (AI) and virtual reality (VR) in the psychiatric field has been rapidly developing. This narrative review seeks to provide insight into how these technologies may be used in psychiatric disorders. V...
New Method of Early RRMS Diagnosis Using OCT-Assessed Structural Retinal Data and Explainable Artificial Intelligence.
Translational vision science & technology
Feb 3, 2025
PURPOSE: The purpose of this study was to provide the development of a method to classify optical coherence tomography (OCT)-assessed retinal data in the context of automatic diagnosis of early-stage multiple sclerosis (MS) with decision explanation.
Beyond electronic health record data: leveraging natural language processing and machine learning to uncover cognitive insights from patient-nurse verbal communications.
Journal of the American Medical Informatics Association : JAMIA
Feb 1, 2025
BACKGROUND: Mild cognitive impairment and early-stage dementia significantly impact healthcare utilization and costs, yet more than half of affected patients remain underdiagnosed. This study leverages audio-recorded patient-nurse verbal communicatio...
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