AI Medical Compendium Topic:
Cross-Sectional Studies

Clear Filters Showing 681 to 690 of 1223 articles

Detection of sarcopenic obesity and prediction of long-term survival in patients with gastric cancer using preoperative computed tomography and machine learning.

Journal of surgical oncology
BACKGROUND: Previous studies evaluating the prognostic value of computed tomography (CT)-derived body composition data have included few patients. Thus, we assessed the prevalence and prognostic value of sarcopenic obesity in a large population of ga...

Assessing the Electronic Evidence System Needs of Canadian Public Health Professionals: Cross-sectional Study.

JMIR public health and surveillance
BACKGROUND: True evidence-informed decision-making in public health relies on incorporating evidence from a number of sources in addition to traditional scientific evidence. Lack of access to these types of data as well as ease of use and interpretab...

Delivery mode and perinatal antibiotics influence the predicted metabolic pathways of the gut microbiome.

Scientific reports
Delivery mode and perinatal antibiotics influence gut microbiome composition in children. Most microbiome studies have used the sequencing of the bacterial 16S marker gene but have not reported the metabolic function of the gut microbiome, which may ...

Accurate diagnosis of sepsis using a neural network: Pilot study using routine clinical variables.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Sepsis is a severe infection that increases mortality risk and is one if the main causes of death in intensive care units. Accurate detection is key to successful interventions, but diagnosis of sepsis is complicated becaus...

Patient Perceptions on Data Sharing and Applying Artificial Intelligence to Health Care Data: Cross-sectional Survey.

Journal of medical Internet research
BACKGROUND: Considerable research is being conducted as to how artificial intelligence (AI) can be effectively applied to health care. However, for the successful implementation of AI, large amounts of health data are required for training and testin...

Towards precision cardiometabolic prevention: results from a machine learning, semi-supervised clustering approach in the nationwide population-based ORISCAV-LUX 2 study.

Scientific reports
Given the rapid increase in the incidence of cardiometabolic conditions, there is an urgent need for better approaches to prevent as many cases as possible and move from a one-size-fits-all approach to a precision cardiometabolic prevention strategy ...

Using normative modelling to detect disease progression in mild cognitive impairment and Alzheimer's disease in a cross-sectional multi-cohort study.

Scientific reports
Normative modelling is an emerging method for quantifying how individuals deviate from the healthy populational pattern. Several machine learning models have been implemented to develop normative models to investigate brain disorders, including regre...

S3Reg: Superfast Spherical Surface Registration Based on Deep Learning.

IEEE transactions on medical imaging
Cortical surface registration is an essential step and prerequisite for surface-based neuroimaging analysis. It aligns cortical surfaces across individuals and time points to establish cross-sectional and longitudinal cortical correspondences to faci...