AIMC Topic: Chronic Disease
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Predicting 28-day all-cause mortality in patients admitted to intensive care units with pre-existing chronic heart failure using the stress hyperglycemia ratio: a machine learning-driven retrospective cohort analysis.
Cardiovascular diabetology
Jan 8, 2025
Chronic heart failure (CHF) poses a significant threat to human health. The stress hyperglycemia ratio (SHR) is a novel metric for accurately assessing stress hyperglycemia, which has been correlated with adverse outcomes in various major diseases. H...
Chronic Disease
Predictive Value of Tests
Cause of Death
Heart Failure
Risk Assessment
Blood Glucose
Time Factors
Hyperglycemia
Patient Admission
Retrospective Studies
Intensive Care Units
Female
Risk Factors
Aged
Aged, 80 and over
Male
Middle Aged
Decision Support Techniques
Databases, Factual
Biomarkers
Prognosis
Humans
Machine Learning
Clinically validated classification of chronic wounds method with memristor-based cellular neural network.
Scientific reports
Dec 28, 2024
Chronic wounds are a syndrome that affects around 4% of the world population due to several pathologies. The COV-19 pandemic has enforced the need of developing new techniques and technologies that can help clinicians to monitor the affected patients...
Development and Validation of an Explainable Prediction Model for Postoperative Recurrence in Pediatric Chronic Rhinosinusitis.
Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
Dec 17, 2024
OBJECTIVE: This study aims to develop an interpretable machine learning (ML) predictive model to assess its efficacy in predicting postoperative recurrence in pediatric chronic rhinosinusitis (CRS).
Artificial intelligence in the care of children and adolescents with chronic diseases: a systematic review.
European journal of pediatrics
Dec 14, 2024
UNLABELLED: The integration of artificial intelligence (AI) and machine learning (ML) has shown potential for various applications in the medical field, particularly for diagnosing and managing chronic diseases among children and adolescents. This sy...
Improvement of an Edge-IoT Architecture Driven by Artificial Intelligence for Smart-Health Chronic Disease Management.
Sensors (Basel, Switzerland)
Dec 13, 2024
One of the health challenges in the 21st century is to rethink approaches to non-communicable disease prevention. A solution is a smart city that implements technology to make health smarter, enables healthcare access, and contributes to all resident...
Using machine learning to predict the probability of incident 2-year depression in older adults with chronic diseases: a retrospective cohort study.
BMC psychiatry
Dec 2, 2024
BACKGROUND: Older adults with chronic diseases are at higher risk of depressive symptoms than those without. For the onset of depressive symptoms, the prediction ability of changes in common risk factors over a 2-year follow-up period is unclear in t...
Facial expression analysis using convolutional neural network for drug-naive and chronic schizophrenia.
Journal of psychiatric research
Nov 29, 2024
OBJECTIVE: Facial images have been shown to convey mental conditions as clinical symptoms. This study aimed to use facial images to detect patients with drug-naive schizophrenia (DN-SCZ) or chronic schizophrenia (C-SCZ) from healthy controls (HCs), a...
Impact of a clinical pharmacist-led, artificial intelligence-supported medication adherence program on medication adherence performance, chronic disease control measures, and cost savings.
Journal of the American Pharmacists Association : JAPhA
Oct 18, 2024
BACKGROUND: Chronic diseases are the leading cause of disability and death in the United States. Clinical pharmacists have been shown to optimize health outcomes and reduce health care expenditures in patients with chronic diseases through improving ...
A preliminary review of the utility of artificial intelligence to detect eosinophilic chronic rhinosinusitis.
International forum of allergy & rhinology
Oct 10, 2024
While typically diagnosed with biopsy, ECRS may be predicted preoperatively with the use of AI. Various AI models have been used, with pooled sensitivity of 0.857 and specificity of 0.850. We found no statistically significant difference between the ...
Effects of end-effector robotic arm reach training with functional electrical stimulation for chronic stroke survivors.
Topics in stroke rehabilitation
Oct 3, 2024
BACKGROUND: Upper-extremity dysfunction significantly affects dependence in the daily lives of stroke survivors, limiting their participation in the social environment and reducing their quality of life.
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