AIMC Topic: Risk Factors

Clear Filters Showing 1441 to 1450 of 2857 articles

Unsupervised machine learning methods and emerging applications in healthcare.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
Unsupervised machine learning methods are important analytical tools that can facilitate the analysis and interpretation of high-dimensional data. Unsupervised machine learning methods identify latent patterns and hidden structures in high-dimensiona...

Key factors selection on adolescents with non-suicidal self-injury: A support vector machine based approach.

Frontiers in public health
Comparing a family structure to a company, one can often think of parents as leaders and adolescents as employees. Stressful family environments and anxiety levels, depression levels, personality disorders, emotional regulation difficulties, and chil...

Exploring risk factors for cervical lymph node metastasis in papillary thyroid microcarcinoma: construction of a novel population-based predictive model.

BMC endocrine disorders
BACKGROUND: Machine learning was a highly effective tool in model construction. We aim to establish a machine learning-based predictive model for predicting the cervical lymph node metastasis (LNM) in papillary thyroid microcarcinoma (PTMC).

Deep Learning Segmentation and Reconstruction for CT of Chronic Total Coronary Occlusion.

Radiology
Background CT imaging of chronic total occlusion (CTO) is useful in guiding revascularization, but manual reconstruction and quantification are time consuming. Purpose To develop and validate a deep learning (DL) model for automated CTO reconstructio...

Biological signatures and prediction of an immunosuppressive status-persistent critical illness-among orthopedic trauma patients using machine learning techniques.

Frontiers in immunology
BACKGROUND: Persistent critical illness (PerCI) is an immunosuppressive status. The underlying pathophysiology driving PerCI remains incompletely understood. The objectives of the study were to identify the biological signature of PerCI development, ...

Clustering of trauma patients based on longitudinal data and the application of machine learning to predict recovery.

Scientific reports
Predicting recovery after trauma is important to provide patients a perspective on their estimated future health, to engage in shared decision making and target interventions to relevant patient groups. In the present study, several unsupervised tech...

Deep Learning Coronary Artery Calcium Scores from SPECT/CT Attenuation Maps Improve Prediction of Major Adverse Cardiac Events.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Low-dose ungated CT attenuation correction (CTAC) scans are commonly obtained with SPECT/CT myocardial perfusion imaging. Despite the characteristically low image quality of CTAC, deep learning (DL) can potentially quantify coronary artery calcium (C...

The Association of Waist Circumference with the Prevalence and Survival of Digestive Tract Cancer in US Adults: A Population Study Based on Machine Learning Methods.

Computational and mathematical methods in medicine
AIMS: This paper aims to investigate the relationship of waist circumference (WC) with digestive tract cancer morbidity and mortality.

Transfusion-associated hyperkalemia in pediatric population: Analyses for risk factors and recommendations.

Transfusion
BACKGROUND: Transfusion-associated hyperkalemia (TAH) is a potentially life-threatening complication of red blood cell (RBC) transfusion. Previously, we reported features of RBC transfusions from 35 pediatric patients (TAH group) who had hyperkalemia...

Using modern risk engines and machine learning/artificial intelligence to predict diabetes complications: A focus on the BRAVO model.

Journal of diabetes and its complications
Management of diabetes requires a multifaceted approach of risk factor reduction; through management of risk factors such as glucose, blood pressure and cholesterol. Goals for these risk factors often vary and guidelines suggest that this is based on...