AIMC Topic: Risk Factors

Clear Filters Showing 1511 to 1520 of 2857 articles

Rates and risk factors of intrapedicular accuracy and cranial facet joint violation among robot-assisted, fluoroscopy-guided percutaneous, and freehand techniques in pedicle screw fixation of thoracolumbar fractures: a comparative cohort study.

BMC surgery
BACKGROUND: Robot-assisted (RA) technique has been increasingly applied in clinical practice, providing promising outcomes of inserting accuracy and cranial facet joint protection. However, studies comparing this novel method with other assisted meth...

Machine learning-based diagnosis and risk factor analysis of cardiocerebrovascular disease based on KNHANES.

Scientific reports
The prevalence of cardiocerebrovascular disease (CVD) is continuously increasing, and it is the leading cause of human death. Since it is difficult for physicians to screen thousands of people, high-accuracy and interpretable methods need to be prese...

Machine learning-aided risk prediction for metabolic syndrome based on 3 years study.

Scientific reports
Metabolic syndrome (MetS) is a group of physiological states of metabolic disorders, which may increase the risk of diabetes, cardiovascular and other diseases. Therefore, it is of great significance to predict the onset of MetS and the corresponding...

Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer.

Nature communications
Long noncoding RNAs (lncRNAs) are recently implicated in modifying immunology in colorectal cancer (CRC). Nevertheless, the clinical significance of immune-related lncRNAs remains largely unexplored. In this study, we develope a machine learning-base...

Help seeking behavior by women experiencing intimate partner violence in india: A machine learning approach to identifying risk factors.

PloS one
BACKGROUND: Despite the low prevalence of help-seeking behavior among victims of intimate partner violence (IPV) in India, quantitative evidence on risk factors, is limited. We use a previously validated exploratory approach, to examine correlates of...

An Interpretable Machine Learning Approach to Predict Fall Risk Among Community-Dwelling Older Adults: a Three-Year Longitudinal Study.

Journal of general internal medicine
BACKGROUND: Adverse health effects resulting from falls are a major public health concern. Although studies have identified risk factors for falls, none have examined long-term prediction of fall risk. Furthermore, recent evidence suggests that there...

Incorporating uncertainty in learning to defer algorithms for safe computer-aided diagnosis.

Scientific reports
Deep neural networks are increasingly being used for computer-aided diagnosis, but erroneous diagnoses can be extremely costly for patients. We propose a learning to defer with uncertainty (LDU) algorithm which identifies patients for whom diagnostic...

Dynamic Bayesian networks for prediction of health status and treatment effect in patients with chronic lymphocytic leukemia.

Scientific reports
Chronic lymphocytic leukemia (CLL) is the most common blood cancer in adults. The course of CLL and patients' response to treatment are varied. This variability makes it difficult to select the most appropriate treatment regimen and predict the progr...

Predicting Atrial Fibrillation Recurrence by Combining Population Data and Virtual Cohorts of Patient-Specific Left Atrial Models.

Circulation. Arrhythmia and electrophysiology
BACKGROUND: Current ablation therapy for atrial fibrillation is suboptimal, and long-term response is challenging to predict. Clinical trials identify bedside properties that provide only modest prediction of long-term response in populations, while ...

Development and validation of a machine learning method to predict intraoperative red blood cell transfusions in cardiothoracic surgery.

Scientific reports
Accurately predicting red blood cell (RBC) transfusion requirements in cardiothoracic (CT) surgery could improve blood inventory management and be used as a surrogate marker for assessing hemorrhage risk preoperatively. We developed a machine learnin...