AIMC Topic: Risk Assessment

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Leveraging machine learning for enhanced and interpretable risk prediction of venous thromboembolism in acute ischemic stroke care.

PloS one
BACKGROUND: Venous thromboembolism (VTE) is a life-threatening complication commonly occurring after acute ischemic stroke (AIS), with an increased risk of mortality. Traditional risk assessment tools lack precision in predicting VTE in AIS patients ...

Artificial intelligence: A key fulcrum for addressing complex environmental health issues.

Environment international
Environmental health (EH) is a complex and interdisciplinary field dedicated to the examination of environmental behaviours, toxicological effects, health risks, and strategies for mitigating harmful environmental factors. Traditional EH research inv...

Artificial intelligence-based risk assessment tools for sexual, reproductive and mental health: a systematic review.

BMC medical informatics and decision making
BACKGROUND: Artificial intelligence (AI), which emulates human intelligence through knowledge-based heuristics, has transformative impacts across various industries. In the global healthcare sector, there is a pressing need for advanced risk assessme...

LI-RADS-based hepatocellular carcinoma risk mapping using contrast-enhanced MRI and self-configuring deep learning.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: Hepatocellular carcinoma (HCC) is often diagnosed using gadoxetate disodium-enhanced magnetic resonance imaging (EOB-MRI). Standardized reporting according to the Liver Imaging Reporting and Data System (LI-RADS) can improve Gd-MRI interp...

Explainable attention-enhanced heuristic paradigm for multi-view prognostic risk score development in hepatocellular carcinoma.

Hepatology international
PURPOSE: Existing prognostic staging systems depend on expensive manual extraction by pathologists, potentially overlooking latent patterns critical for prognosis, or use black-box deep learning models, limiting clinical acceptance. This study introd...

Interpretable machine learning for thyroid cancer recurrence predicton: Leveraging XGBoost and SHAP analysis.

European journal of radiology
PURPOSE: For patients suffering from differentiated thyroid cancer (DTC), several clinical, laboratory, and pathological features (including patient age, tumor size, extrathyroidal extension, or serum thyroglobulin levels) are currently used to ident...

Risk prediction of hyperuricemia based on particle swarm fusion machine learning solely dependent on routine blood tests.

BMC medical informatics and decision making
Hyperuricemia has seen a continuous increase in incidence and a trend towards younger patients in recent years, posing a serious threat to human health and highlighting the urgency of using technological means for disease risk prediction. Existing ri...

An explainable web application based on machine learning for predicting fragility fracture in people living with HIV: data from Beijing Ditan Hospital, China.

Frontiers in cellular and infection microbiology
PURPOSE: This study aimed to develop and validate a novel web-based calculator using machine learning algorithms to predict fragility fracture risk in People living with HIV (PLWH), who face increased morbidity and mortality from such fractures.

Operational safety risk modeling in a naval organization.

Journal of safety research
INTRODUCTION: Following numerous mishaps and near-misses, the U.S. Naval Surface Force established the Operational Surface Risk Indicators (OSRI) project to explore a robust proactive risk analysis and reduction capability. The OSRI model leverages m...

Risk of bias assessment of post-stroke mortality machine learning predictive models: Systematic review.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
BACKGROUND: Stroke is a major cause of mortality and permanent disability worldwide. Precise prediction of post-stroke mortality is essential for guiding treatment decisions and rehabilitation planning. The ability of Machine learning models to proce...