AIMC Topic: Machine Learning

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Pancreatic Cancer Detection and Differentiation from Chronic Pancreatitis: Potential Biomarkers Identified through a High-Throughput Multiplex Proteomic Assay and Machine Learning-Based Analysis.

Annals of laboratory medicine
BACKGROUND: Pancreatic cancer (PC)-screening methods have limited accuracy despite their high clinical demand. Differential diagnosis of chronic pancreatitis (CP) poses another challenge for PC diagnosis. Therefore, we aimed to identify blood protein...

Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial.

Nature medicine
The COmmunicating Narrative Concerns Entered by RNs (CONCERN) early warning system (EWS) uses real-time nursing surveillance documentation patterns in its machine learning algorithm to identify deterioration risk. We conducted a 1-year, multisite, pr...

Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study.

JMIR aging
BACKGROUND: Frailty is defined as a clinical state of increased vulnerability due to the age-associated decline of an individual's physical function resulting in increased morbidity and mortality when exposed to acute stressors. Early identification ...

Electrocardiographic Discrimination of Long QT Syndrome Genotypes: A Comparative Analysis and Machine Learning Approach.

Sensors (Basel, Switzerland)
Long QT syndrome (LQTS) presents a group of inheritable channelopathies with prolonged ventricular repolarization, leading to syncope, ventricular tachycardia, and sudden death. Differentiating LQTS genotypes is crucial for targeted management and tr...

Machine learning and multi-omics integration: advancing cardiovascular translational research and clinical practice.

Journal of translational medicine
The global burden of cardiovascular diseases continues to rise, making their prevention, diagnosis and treatment increasingly critical. With advancements and breakthroughs in omics technologies such as high-throughput sequencing, multi-omics approach...

Improved prediction and risk stratification of major adverse cardiovascular events using an explainable machine learning approach combining plasma biomarkers and traditional risk factors.

Cardiovascular diabetology
BACKGROUND: Cardiovascular diseases (CVD) remain the leading cause of morbidity and mortality globally. Traditional risk models, primarily based on established risk factors, often lack the precision needed to accurately predict new-onset major advers...

Definition of the microbial rare biosphere through unsupervised machine learning.

Communications biology
The microbial rare biosphere, composed of low-abundance microorganisms in a community, lacks a standardized delineation method for its definition. Currently, most studies rely on arbitrary thresholds to define the microbial rare biosphere (e.g., 0.1%...

An ensemble learning model to predict lymph node metastasis in early gastric cancer.

Scientific reports
Lymph node metastasis is a critical factor for determining therapeutic strategies and assessing the prognosis of early gastric cancer. This work aimed to establish a more dependable predictive model for identify lymph node metastasis in early gastric...

Enhanced prediction of ventilator-associated pneumonia in patients with traumatic brain injury using advanced machine learning techniques.

Scientific reports
Ventilator-associated pneumonia significantly increases morbidity, mortality, and healthcare costs among patients with traumatic brain injury. Accurately predicting risk can facilitate earlier interventions and improve patient outcomes. This study le...

Machine learning in point-of-care testing: innovations, challenges, and opportunities.

Nature communications
The landscape of diagnostic testing is undergoing a significant transformation, driven by the integration of artificial intelligence (AI) and machine learning (ML) into decentralized, rapid, and accessible sensor platforms for point-of-care testing (...