Hypertension

First-Pass Reperfusion After Endovascular Thrombectomy: A Real-World Analysis with Explainable Machine Learning for Intra-Procedural Decision Support.

OBJECTIVE: To evaluate determinants of first-pass reperfusion and to develop an explainable machine learning framework for intra-procedural decision support in patients undergoing endovascular thrombectomy. MATERIALS AND METHODS: In this retrospective single-center study, 204 consecutive patients with acute ischemic stroke treated with endovascular thrombectomy between June 2021 and June 2025 were...

Interpretable machine learning reveals the synergistic impact of essential hypertension on early recurrence in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) patients often ential hypertension, yet how systemic vascular stress synergizes with tumor aggressiveness to drive early recurrence remains poorly understood. A cohort of 549 TNBC patients with comorbid hypertension was analyzed. Fifteen multidimensional clinical and hemodynamic features were extracted. We constructed and compared four machine learning models, ...

Artificial intelligence-based prediction of diseases among homeless populations in Bogotá: Implications for targeted interventions.

BACKGROUND: The homeless population in Bogotá exhibits complex social and health vulnerabilities, with a high prevalence of chronic and communicable d...

Machine learning and plasma growth differentiation factor-15 (GDF15) for early risk stratification of heart failure progression in a long-latency doxorubicin cardiomyopathy model.

BACKGROUND: Anthracycline-induced cardiotoxicity is a major cause of late heart failure (HF) in cancer survivors. Yet early identification of individu...

Serial AI-Enabled Electrocardiogram Trajectories During Heart Failure Treatment: A Proof-of-Concept Case Series.

BACKGROUND: Artificial intelligence-enabled electrocardiogram (AI-ECG) detects left ventricular systolic and diastolic dysfunction at single time poin...

An Explainable AI Framework for Continuous Monitoring, Risk Stratification, and Clinical Decision Support in Primary Biliary Cholangitis: Protocol for a Multiphase Development and Validation Study.

BACKGROUND: Primary biliary cholangitis (PBC) management remains limited by reliance on static biochemical markers, fragmented assessment of symptom b...

Predicting intradialytic exercise intolerance in maintenance hemodialysis patients: an interpretable machine learning approach integrating functional assessments.

Exercise intolerance, primarily manifesting as intradialytic hypotension (IDH), is common in maintenance hemodialysis patients, interrupting intervent...

Predicting mortality risk in hospitalized ACS patients with hypertensive comorbidity: an interpretable machine learning approach.

BACKGROUND: Hypertension serves as a prevalent health issue, particularly in South Asia, where it is also a risk factor and comorbidity that affects t...

Diastolic dysfunction is linked to the initiation and progression of aortic stenosis: a hypothesis.

The conventional paradigm in aortic stenosis (AS) holds that progressive valvular obstruction increases afterload, leading to left ventricular remodel...

Left Ventricular Thrombus in Ischemic Heart Failure: Machine-learning-based Prediction of Six-month Persistence and One-year Outcomes.

Left ventricular thrombus (LVT) in ischemic heart failure carries embolic risk; tools to anticipate persistence are limited. We studied 190 consecutiv...

EMBC Special Issue: Non-invasive BP Estimation via Carotid-Brachial-Radial Coupling: A Physics-Informed Asymmetric Cycle Network Approach.

Photoplethysmogram (PPG) based cuffless blood pressure (BP) monitoring holds significant potential to revolutionize hypertension management. However, ...

An interpretable combinatorial data-mining framework for predicting new-onset hypertension in the general population.

We previously established an interpretable combinatorial data-mining framework to identify combinations of clinical factors predictive of heart failur...

PCT-Anchored Machine Learning for Pre-Culture Identification of Gram-Negative Sepsis in Children: A Four-Site Study.

OBJECTIVE: Procalcitonin (PCT) has moderate accuracy for bacteremia detection but is infrequently used in pediatric emergency departments, partly due ...

An interpretable machine learning model for predicting postoperative hypotension in type 2 diabetes mellitus undergoing non‑cardiac surgery.

BACKGROUND: Postoperative hypotension (POH) is a common and serious complication in patients with type 2 diabetes mellitus (T2DM) undergoing non‑cardi...

Contrastive Machine Learning to Quantify Hypertensive Multiorgan Damage and Identify New Disease Phenotypes: A Multinational Multimodal Study.

BACKGROUND: Hypertension induces structural and functional damage in multiple organs. Evidence of subclinical damage increases risk of vascular events...

Phenotyping of pulmonary arterial hypertension associated with congenital heart disease using latent class analysis: insights from a national prospective registry.

BACKGROUND: Pulmonary arterial hypertension associated with congenital heart disease (PAH-CHD) is characterized by substantial prognostic and therapeu...

A machine learning-based framework for predicting hypertension using serum hematological factors.

Hypertension (HTN) is a leading global cause of cardiovascular disease (CVD) and all-cause mortality, underscoring the need for early detection and in...

AutoRV: Automated Assessment of Right Ventricular Structure and Function Using Transesophageal Echocardiography in Mechanically Ventilated Patients.

OBJECTIVE: Right ventricle (RV) dysfunction has therapeutic implications for the management of mechanically ventilated patients in intensive care unit...

The role of metabolic health in neurostructural and cognitive alterations in bipolar disorders.

BACKGROUND: Bipolar disorders (BD) rank among the most disabling conditions, affecting millions worldwide. Cognitive impairment in BD is linked with b...

Past, Present, and Future of Evaluating Diastolic Function.

The assessment of left ventricular diastolic function started with invasive pressure measurements and is currently primarily based on echocardiographi...