AI Medical Compendium Topic:
Risk Assessment

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Federated-learning-based prognosis assessment model for acute pulmonary thromboembolism.

BMC medical informatics and decision making
BACKGROUND: Acute pulmonary thromboembolism (PTE) is a common cardiovascular disease and recognizing low prognosis risk patients with PTE accurately is significant for clinical treatment. This study evaluated the value of federated learning (FL) tech...

Role of machine learning algorithms in suicide risk prediction: a systematic review-meta analysis of clinical studies.

BMC medical informatics and decision making
OBJECTIVE: Suicide is a complex and multifactorial public health problem. Understanding and addressing the various factors associated with suicide is crucial for prevention and intervention efforts. Machine learning (ML) could enhance the prediction ...

The potential use of artificial intelligence for venous thromboembolism prophylaxis and management: clinician and healthcare informatician perspectives.

Scientific reports
Venous thromboembolism (VTE) is the leading cause of preventable death in hospitalized patients. Artificial intelligence (AI) and machine learning (ML) can support guidelines recommending an individualized approach to risk assessment and prophylaxis....

Real-time driving risk prediction using a self-attention-based bidirectional long short-term memory network based on multi-source data.

Accident; analysis and prevention
Early warning of driving risks can effectively prevent collisions. However, numerous studies that predicted driving risks have suffered from the use of single data sources, insufficiently advanced models, and lack of time window analysis. To address ...

Navigating the decision-making landscape of AI in risk finance: Techno-accountability unveiled.

Risk analysis : an official publication of the Society for Risk Analysis
The integration of artificial intelligence (AI) systems has ushered in a profound transformation. This conversion is marked by revolutionary extrapolative capabilities, a shift toward data-centric decision-making processes, and the enhancement of too...

Prediction of 30-Day Mortality Following Revision Total Hip and Knee Arthroplasty: Machine Learning Algorithms Outperform CARDE-B, 5-Item, and 6-Item Modified Frailty Index Risk Scores.

The Journal of arthroplasty
BACKGROUND: Although risk calculators are used to prognosticate postoperative outcomes following revision total hip and knee arthroplasty (total joint arthroplasty [TJA]), machine learning (ML) based predictive tools have emerged as a promising alter...

Deep Learning Models for Predicting Malignancy Risk in CT-Detected Pulmonary Nodules: A Systematic Review and Meta-analysis.

Lung
BACKGROUND: There has been growing interest in using artificial intelligence/deep learning (DL) to help diagnose prevalent diseases earlier. In this study we sought to survey the landscape of externally validated DL-based computer-aided diagnostic (C...

Artificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time?

Current atherosclerosis reports
PURPOSE OF REVIEW: This review evaluates how Artificial Intelligence (AI) enhances atherosclerotic cardiovascular disease (ASCVD) risk assessment, allows for opportunistic screening, and improves adherence to guidelines through the analysis of unstru...

Risk assessment of organ transplant operation: A fuzzy hybrid MCDM approach based on fuzzy FMEA.

PloS one
Nowadays, most fatal diseases are attributed to the malfunction of bodily. Sometimes organ transplantation is the only possible therapy, for instance for patients with end-stage liver diseases, and the preferred treatment, for instance for patients w...