AIMC Topic: Risk Assessment
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Multimodal feature fusion machine learning for predicting chronic injury induced by engineered nanomaterials.
Nature communications
Mar 20, 2025
Concerns regarding chronic injuries (e.g., fibrosis and carcinogenesis) induced by nanoparticles raised public health concerns and need to be rapidly assessed in hazard identification. Although in silico analysis is commonly used for risk assessment ...
Personalized prediction of psoriasis relapse post-biologic discontinuation: a machine learning-driven population cohort study.
The Journal of dermatological treatment
Mar 19, 2025
BACKGROUND: Identifying the risk of psoriasis relapse after discontinuing biologics can help optimize treatment strategies, potentially reducing relapse rates and alleviating the burden of disease management.
Risk assessment of potentially toxic elements and mapping of groundwater pollution indices using soft computer models in an agricultural area, Northeast Algeria.
Journal of hazardous materials
Mar 19, 2025
Groundwater (GW) quality and contamination by potentially toxic elements (PTEs) are major concerns for environmental sustainability, particularly in arid regions. The aim of this study was to assess the human health risks associated with GW contamina...
Perioperative risk assessment for emergency general surgery in those with multimorbidity or frailty.
Current opinion in critical care
Mar 19, 2025
PURPOSE OF REVIEW: This review explores advances in risk stratification tools and their applicability in identifying and managing high-risk emergency general surgery (EGS) patients.
Machine learning models for enhanced diagnosis and risk assessment of prostate cancer with Ga-PSMA-617 PET/CT.
European journal of radiology
Mar 19, 2025
OBJECTIVE: Prostate cancer (PCa) is highly heterogeneous, making early detection of adverse pathological features crucial for improving patient outcomes. This study aims to predict PCa aggressiveness and identify radiomic and protein biomarkers assoc...
Gallium Isotopes
Heterocyclic Compounds, 1-Ring
Prostatic Neoplasms
Aged
Prostate-Specific Antigen
Humans
Aged, 80 and over
Radiopharmaceuticals
Male
Machine Learning
Sensitivity and Specificity
Dipeptides
Retrospective Studies
Biomarkers, Tumor
Gallium Radioisotopes
Middle Aged
Risk Assessment
Positron Emission Tomography Computed Tomography
Predicting the risks of stroke, cardiovascular disease, and peripheral vascular disease among people with type 2 diabetes with artificial intelligence models: A systematic review and meta-analysis.
Narra J
Mar 19, 2025
Macrovascular complications, including stroke, cardiovascular disease (CVD), and peripheral vascular disease (PVD), significantly contribute to morbidity and mortality in individuals with type 2 diabetes mellitus (T2DM). The aim of this study was to ...
A Machine Learning Prediction Model to Identify Individuals at Risk of 5-Year Incident Stroke Based on Retinal Imaging.
Sensors (Basel, Switzerland)
Mar 19, 2025
Stroke is a leading cause of death and disability in developed countries. We validated an AI-based prediction model for incident stroke using sensors such as fundus cameras and ophthalmoscopes for retinal images, along with socio-demographic data and...
Predicting postoperative pulmonary infection in elderly patients undergoing major surgery: a study based on logistic regression and machine learning models.
BMC pulmonary medicine
Mar 19, 2025
BACKGROUND: Postoperative pulmonary infection (POI) is strongly associated with a poor prognosis and has a high incidence in elderly patients undergoing major surgery. Machine learning (ML) algorithms are increasingly being used in medicine, but the ...
Comparison of machine learning and nomogram to predict 30-day in-hospital mortality in patients with acute myocardial infarction combined with cardiogenic shock: a retrospective study based on the eICU-CRD and MIMIC-IV databases.
BMC cardiovascular disorders
Mar 19, 2025
BACKGROUND: To evaluate the predictive utility of machine learning and nomogram in predicting in-hospital mortality in patients with acute myocardial infarction complicated by cardiogenic shock (AMI-CS), and to visualize the model results in order to...
Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: data from the Araraquara cohort study.
BMC pregnancy and childbirth
Mar 19, 2025
BACKGROUND: Low birth weight (LBW) is a critical factor linked to neonatal morbidity and mortality. Early prediction is essential for timely interventions. This study aimed to develop and evaluate predictive models for LBW using machine learning algo...
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