AIMC Topic: Middle Aged

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Prognosis of major bleeding based on residual variables and machine learning for critical patients with upper gastrointestinal bleeding: A multicenter study.

Journal of critical care
BACKGROUND: Upper gastrointestinal bleeding (UGIB) is a significant cause of morbidity and mortality worldwide. This study investigates the use of residual variables and machine learning (ML) models for predicting major bleeding in patients with seve...

A machine learning algorithm for the identification elevated Lp(a) in patients with, or high-risk of having, coronary heart disease.

International journal of cardiology
BACKGROUND: Decision tree algorithms, obtained by machine learning, provide clusters of patients with similar clinical patterns by the identification of variables that best merge with a given dependent variable.

A Robotic Clamped-Kinematic System to Study Knee Ligament Injury.

Annals of biomedical engineering
Knee ligament injury is among the most common sports injuries and is associated with long recovery periods and low return-to-sport rates. Unfortunately, the mechanics of ligament injury are difficult to study in vivo, and computational studies provid...

Rapid and noninvasive estimation of human arsenic exposure based on 4-photo-set of the hand and foot photos through artificial intelligence.

Journal of hazardous materials
Chronic exposure to arsenic is linked to the development of cancers in the skin, lungs, and bladder. Arsenic exposure manifests as variegated pigmentation and characteristic pitted keratosis on the hands and feet, which often precede the onset of int...

Development and validation of a sepsis risk index supporting early identification of ICU-acquired sepsis: an observational study.

Anaesthesia, critical care & pain medicine
BACKGROUND: Sepsis is a threat to global health, and domestically is the major cause of in-hospital mortality. Due to increases in inpatient morbidity and mortality resulting from sepsis, healthcare providers (HCPs) would accrue significant benefits ...

Machine learning models for discriminating clinically significant from clinically insignificant prostate cancer using bi-parametric magnetic resonance imaging.

Diagnostic and interventional radiology (Ankara, Turkey)
PURPOSE: This study aims to demonstrate the performance of machine learning algorithms to distinguish clinically significant prostate cancer (csPCa) from clinically insignificant prostate cancer (ciPCa) in prostate bi-parametric magnetic resonance im...

Machine Learning-Based Clustering Using a 12-Lead Electrocardiogram in Patients With a Implantable Cardioverter Defibrillator to Identify Future Ventricular Arrhythmia.

Circulation journal : official journal of the Japanese Circulation Society
BACKGROUND: Implantable cardioverter defibrillators (ICDs) reduce mortality associated with ventricular arrhythmia in high-risk patients with cardiovascular disease. Machine learning (ML) approaches are promising tools in arrhythmia research; however...

Development of the machine learning model that is highly validated and easily applicable to predict radiographic knee osteoarthritis progression.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society
Many models using the aid of artificial intelligence have been recently proposed to predict the progression of knee osteoarthritis. However, previous models have not been properly validated with an external data set or have reported poor predictive p...