AIMC Topic: Risk Factors

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[A Thyroid Ultrasound Image-based Artificial Intelligence Model for Diagnosis of Central Compartment Lymph Node Metastasis in Papillary Thyroid Carcinoma].

Zhongguo yi xue ke xue yuan xue bao. Acta Academiae Medicinae Sinicae
Objective To establish an artificial intelligence model based on B-mode thyroid ultrasound images to predict central compartment lymph node metastasis(CLNM)in patients with papillary thyroid carcinoma(PTC). Methods We retrieved the clinical manifesta...

Correlation among cystatin C, homocysteine and arteriosclerosis indexes in patients with chronic kidney disease.

Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences
OBJECTIVES: Chronic kidney disease (CKD) has become an important public health problem in the world. The occurrence of cardiovascular events is the main cause of death in patients with CKD, and arteriosclerosis is an important pathophysiological basi...

Applications of Artificial Intelligence in Pediatric Oncology: A Systematic Review.

JCO clinical cancer informatics
PURPOSE: There is a need for an improved understanding of clinical and biologic risk factors in pediatric cancer to improve patient outcomes. Machine learning (ML) represents the application of computational inference from advanced statistical method...

Electrocardiography-Based Artificial Intelligence Algorithm Aids in Prediction of Long-term Mortality After Cardiac Surgery.

Mayo Clinic proceedings
OBJECTIVE: To assess whether an electrocardiography-based artificial intelligence (AI) algorithm developed to detect severe ventricular dysfunction (left ventricular ejection fraction [LVEF] of 35% or below) independently predicts long-term mortality...

Augmenting Osteoporosis Imaging with Machine Learning.

Current osteoporosis reports
PURPOSE OF REVIEW: In this paper, we discuss how recent advancements in image processing and machine learning (ML) are shaping a new and exciting era for the osteoporosis imaging field. With this paper, we want to give the reader a basic exposure to ...

Predicting Sex-Specific Nonfatal Suicide Attempt Risk Using Machine Learning and Data From Danish National Registries.

American journal of epidemiology
Suicide attempts are a leading cause of injury globally. Accurate prediction of suicide attempts might offer opportunities for prevention. This case-cohort study used machine learning to examine sex-specific risk profiles for suicide attempts in Dani...

Machine learning-driven identification of early-life air toxic combinations associated with childhood asthma outcomes.

The Journal of clinical investigation
Air pollution is a well-known contributor to asthma. Air toxics are hazardous air pollutants that cause or may cause serious health effects. Although individual air toxics have been associated with asthma, only a limited number of studies have specif...

Machine Learning Model for Predicting CVD Risk on NHANES Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Cardiovascular disease (CVD) is a major health problem throughout the world. It is the leading cause of morbidity and mortality and also causes considerable economic burden to society. The early symptoms related to previous observations and abnormal ...

A Novel Predictive Model for Anastomotic Leakage in Colorectal Cancer Using Auto-artificial Intelligence.

Anticancer research
AIM: Anastomotic leakage (AL) in left-sided colorectal cancer is a serious complication, with an incidence rate of 6-18%. We developed a novel predictive model for AL in colorectal surgery with double-stapling technique (DST) anastomosis using auto-a...

Predicting the Aortic Aneurysm Postoperative Risks Based on Russian Integrated Data.

Studies in health technology and informatics
This article describes the results of feature extraction from unstructured medical records and prediction of postoperative complications for patients with thoracic aortic aneurysm operations using machine learning algorithms. The datasets from two di...