AIMC Topic: Risk Factors

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Detecting High-Risk Factors and Early Diagnosis of Diabetes Using Machine Learning Methods.

Computational intelligence and neuroscience
Diabetes is a chronic disease that can cause several forms of chronic damage to the human body, including heart problems, kidney failure, depression, eye damage, and nerve damage. There are several risk factors involved in causing this disease, with ...

Circulating serum metabolites as predictors of dementia: a machine learning approach in a 21-year follow-up of the Whitehall II cohort study.

BMC medicine
BACKGROUND: Age is the strongest risk factor for dementia and there is considerable interest in identifying scalable, blood-based biomarkers in predicting dementia. We examined the role of midlife serum metabolites using a machine learning approach a...

Deep Learning-Based Nuclear Morphometry Reveals an Independent Prognostic Factor in Mantle Cell Lymphoma.

The American journal of pathology
Blastoid/pleomorphic morphology is associated with short survival in mantle cell lymphoma (MCL), but its prognostic value is overridden by Ki-67 in multivariate analysis. Herein, a nuclear segmentation model was developed using deep learning, and nuc...

Deep learning artificial intelligence framework for multiclass coronary artery disease prediction using combination of conventional risk factors, carotid ultrasound, and intraplaque neovascularization.

Computers in biology and medicine
OBJECTIVE: Cardiovascular disease (CVD) is a major healthcare challenge and therefore early risk assessment is vital. Previous assessment techniques use either "conventional CVD risk calculators (CCVRC)" or machine learning (ML) paradigms. These tech...

Predicting restriction of life-space mobility: a machine learning analysis of the IMIAS study.

Aging clinical and experimental research
BACKGROUND: Some studies have employed machine learning (ML) methods for mobility prediction modeling in older adults. ML methods could be a helpful tool for life-space mobility (LSM) data analysis.

A machine learning approach to predicting early and late postoperative reintubation.

Journal of clinical monitoring and computing
Accurate estimation of surgical risks is important for informing the process of shared decision making and informed consent. Postoperative reintubation (POR) is a severe complication that is associated with postoperative morbidity. Previous studies h...

Utility of Normalized Body Composition Areas, Derived From Outpatient Abdominal CT Using a Fully Automated Deep Learning Method, for Predicting Subsequent Cardiovascular Events.

AJR. American journal of roentgenology
CT-based body composition (BC) measurements have historically been too resource intensive to analyze for widespread use and have lacked robust comparison with traditional weight metrics for predicting cardiovascular risk. The aim of this study was ...

Relationship between Brachial-Ankle Pulse Wave Velocity and Fundus Arteriolar Area Calculated Using a Deep-Learning Algorithm.

Current eye research
PURPOSE: Retinal vessels reflect alterations related to hypertension and arteriosclerosis in the physical status. Previously, we had reported a deep-learning algorithm for automatically detecting retinal vessels and measuring the total retinal vascul...

Development and Application of an Intelligent Diagnosis System for Retinal Vein Occlusion Based on Deep Learning.

Disease markers
This study is aimed at developing an intelligent algorithm based on deep learning and discussing its application for the classification and diagnosis of retinal vein occlusions (RVO) using fundus images. A total of 501 fundus images of healthy eyes a...