AIMC Topic: Risk Assessment

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Evaluating the ability of artificial intelligence to predict suicide: A systematic review of reviews.

Journal of affective disorders
INTRODUCTION: Suicide remains a critical global public health issue, with approximately 800,000 deaths annually. Despite various prevention efforts, suicide rates are rising, highlighting the need for more effective strategies. Traditional suicide ri...

Interpretable lung cancer risk prediction using ensemble learning and XAI based on lifestyle and demographic data.

Computational biology and chemistry
Lung cancer is a leading cause of cancer-related death worldwide. The early and accurate detection of lung cancer is crucial for improving patient outcomes. Traditional predictive models often lack the accuracy and interpretability required in clinic...

Phenotypic clustering analysis of patients rejected for mitral valve interventions: implications for future transcatheter technologies.

European heart journal. Cardiovascular Imaging
AIMS: Although several treatment options are available for patients with severe mitral regurgitation (MR), a significant proportion of patients remain ineligible for any mitral valve (MV) intervention. We aimed to analyse the phenotypic characteristi...

Deep learning for echocardiographic assessment and risk stratification of aortic, mitral, and tricuspid regurgitation: the DELINEATE-regurgitation study.

European heart journal
BACKGROUND AND AIMS: Classification and risk stratification in aortic (AR), mitral (MR), and tricuspid regurgitation (TR) remains a significant clinical challenge. This study aimed to develop an artificial intelligence (AI) system to assess valvular ...

A Transformer-Based Framework for Counterfactual Estimation of Antihypertensive Treatment Effect on COVID-19 Infection Risk - A Proof-of-Concept Study.

American journal of hypertension
BACKGROUND: Transformer-based neural networks excel in modelling high-dimensional, time-series data with complex dependencies. This proof-of-concept study applies a transformer-X-learner framework to estimate treatment effects using real-world data, ...

Machine learning-driven risk prediction and feature identification for major depressive disorder and its progression: an exploratory study based on five years of longitudinal data from the US national health survey.

Journal of affective disorders
BACKGROUND: Major depressive disorder (MDD) presents significant public health challenges due to its increasing prevalence and complex risk factors. This study systematically analyzed data from 2019 to 2023 to explore trends in MDD incidence, symptom...

Artificial Intelligence to Improve Blood Pressure Control: A State-of-the-Art Review.

American journal of hypertension
Hypertension remains a major global health challenge, contributing to significant morbidity and mortality. Advances in artificial intelligence (AI) and machine learning (ML) are transforming hypertension care by enhancing blood pressure (BP) measurem...

State-of-the-art analysis of electrocardiogram findings in sudden cardiac death.

Heart (British Cardiac Society)
Sudden cardiac death (SCD) is a significant public health issue, and efforts to prevent it have involved the analysis of various modalities, including echocardiography, cardiac CT, cardiac MRI, genetic testing and ECG. The ECG, invented >100 years ag...

Novel composite health assessment risk model for older allogeneic transplant recipients: BMT-CTN 1704.

Blood advances
Allogeneic hematopoietic cell transplantation (allo-HCT) is potentially curative for older adults with hematologic malignancies. Concerns on nonrelapse mortality (NRM) in older adults limit allo-HCT utilization. We executed a prospective, observation...

Phenotype-driven risk stratification of cerebral aneurysms using Shapley Additive Explanations-based supervised clustering: a novel approach to rupture prediction.

Neurosurgical focus
OBJECTIVE: The aim of this study was to address the limitations of traditional aneurysm risk scoring systems and computational fluid dynamics (CFD) analyses by applying a supervised clustering framework to identify distinct aneurysm phenotypes and im...