AIMC Topic: Risk Assessment

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Development of a deep learning model for survival prediction in heart failure: competing risk and frailty model.

Scientific reports
This study presents a novel deep learning (DL) framework, the Deep Neural Frailty Competing Risks (DNFCR) model, which simultaneously integrates frailty and competing risks (CR) for mortality prediction in heart failure (HF). While existing models li...

Clinical Risk Computation by Large Language Models Using Validated Risk Scores.

Journal of medical systems
Recent advances in artificial intelligence have propelled Large Language Models (LLMs) in natural language understanding, enabling new healthcare applications. While LLMs can analyze health data, directly predicting patient risk scores can be unrelia...

Predictive modeling of tax compliance risks: A comparative study of machine learning approaches.

PloS one
Modern enterprises grapple with complex financial data and multidimensional risk interdependencies in their operations. Machine learning offers transformative potential for tax risk assessment and smart auditing solutions. This research analyzes 3,23...

Predicting risk of early-onset sepsis in low-resource neonatal units using routine healthcare data: development and evaluation of multivariable statistical and machine learning models.

BMJ paediatrics open
BACKGROUND: Neonatal sepsis is a major cause of morbidity and mortality in low-resource settings and accurate, context-appropriate diagnostic methods are urgently needed to improve clinical outcomes.

The Impact of Comorbidity Patterns on Clinical Outcomes in Heart Failure: A Machine Learning-Based Cluster Analysis.

The American journal of cardiology
Heart failure (HF) is a major global health burden, and complex comorbidity patterns can worsen clinical outcomes and complicate patient care. This study aimed to identify distinct comorbidity-based clusters among HF patients and evaluate their assoc...

Construction of a predictive model for the risk of moderate-to-severe cancer-related fatigue in colorectal cancer chemotherapy patients: an interpretable machine learning approach.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
PURPOSE: This study aimed to analyze the influencing factors of moderate-to-severe cancer-related fatigue (CRF) in colorectal cancer (CRC) chemotherapy patients and to develop a predictive risk stratification model.

Machine learning-based prediction of post-operative outcomes in robotic-assisted radical prostatectomy: a multi-variable analysis of 758 cases.

Journal of robotic surgery
Robotic-assisted radical prostatectomy (RARP) has become the gold standard treatment for localized prostate cancer. However, predicting post-operative outcomes remains challenging. This study aims to develop and validate predictive models for key out...

Development and validation of a machine learning model for cardiovascular disease risk prediction in type 2 diabetes patients.

Scientific reports
Patients with type 2 diabetes mellitus (T2DM) have a significantly higher risk of cardiovascular disease (CVD) compared to the general population. Accurately predicting this risk is crucial for developing personalized treatment plans and public healt...