AIMC Topic: C-Reactive Protein

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COVID-19 severity analysis for clinical decision support based on machine learning approach.

Scientific reports
The COVID-19 pandemic has placed immense pressure on global healthcare systems, underscoring the urgent need for early and accurate prediction of disease severity to improve patient care and optimize resource allocation. Failure in ward allocation ca...

Machine learning classification of inflammatory bowel disease activity using white blood cell subsets.

BMJ open gastroenterology
OBJECTIVE: The lack of a rapid, validated, consistent test for tracking disease activity in patients with inflammatory bowel disease (IBD) is currently a major challenge. Currently used biomarkers have notable disadvantages, such as the slow processi...

Relationship between C-reactive protein triglyceride glucose index and cardiovascular disease risk: a cross-sectional analysis with machine learning.

BMC medical informatics and decision making
BACKGROUND: Cardiovascular disease (CVD) continues to be a leading cause of disease burden and mortality worldwide. Identifying reliable biomarkers for CVD risk assessment is essential. This study investigates the association between the C-reactive p...

C-reactive protein-triglyceride glucose index in predicting three-vessel coronary artery disease risk: a retrospective study using machine learning approaches.

Annals of medicine
BACKGROUND: Three-vessel coronary artery disease (TVD) is a severe subtype of coronary heart disease, strongly associated with inflammation and metabolic dysfunction. The C-reactive protein-triglyceride glucose index (CTI), an integrated measure of i...

A machine learning model including pentraxin-3 as predictor of outcomes in community-acquired pneumonia.

Journal of translational medicine
BACKGROUND: The clinical diagnosis, severity assessment, and outcome prognostication of community-acquired pneumonia (CAP) remain challenging due to the complex disease pathophysiology. Accurate outcome prediction is crucial for optimizing patient ma...

Assessing the association between multiple indicators of inflammation and sleep disorders in young and middle-aged women: insights from traditional and machine learning approaches.

European journal of medical research
BACKGROUND: Interactions between inflammation and sleep disorders are increasingly recognized; however, limited research comprehensively evaluates the association between multiple inflammatory indicators and sleep disorders.

Impact of the oxidative balance score on cardiovascular-kidney-metabolic syndrome: A cross-sectional study with machine learning prediction.

PloS one
BACKGROUND AND AIM: The antioxidant diet and lifestyle are widely believed to prevent and even treat various diseases; however, their applicability to cardiovascular-kidney-metabolic (CKM) syndrome remains unknown. In this study, the correlation betw...

A digital fluorescence immunoassay platform using a self-driven microfluidic cartridge with magnetic capture.

Biosensors & bioelectronics
Self-driven microfluidic devices have significantly advanced point-of-care testing (POCT) by enabling integration and automation of all the components required for biochemical analysis through capillary-driven fluid flow, eliminating the need for ext...

Machine learning comparison for biomarker level estimation in wastewater dynamics monitoring.

Scientific reports
Wastewater surveillance is an emerging strategy that enables monitoring of the presence and dynamic changes of targeted substances, facilitating improved allocation of preventive actions and public health interventions. This paper investigates the ap...

Development and validation of a machine learning-based predictive model for clinical remission in Crohn's disease patients receiving Adalimumab therapy.

PloS one
Crohn's disease (CD), a chronic inflammatory bowel disease, is witnessing a rising global incidence. Adalimumab (ADA), a biological agent, is widely used in its treatment. However, patients exhibit significant individual variability in responses to A...