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A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data.

BACKGROUND: Venous thromboembolisms (VTEs), which include deep vein thrombosis (DVT) and pulmonary embolism (PE), are associated with significant mortality, morbidity, and cost in hospitalized patients. To evaluate the success of preventive measures, accurate and efficient methods for monitoring VTE rates are needed. Therefore, we sought to determine the accuracy of statistical natural language pr...

Re: One- vs 4-week stent placement after laparoscopic and robot-assisted pyeloplasty: results of a prospective randomised single-centre study.

PINN-ing the Balloon: A Physically Informed Neural Network Modelling the Nonlinear Haemodynamic Response Function in MRI

Accurate characterisation of the haemodynamic response function (HRF) is central to interpreting blood-oxygen-level-dependent (BOLD) signals in functi...

Machine Learning-Supported Efficient VTE Risk Assessment using Routinely Collected Electronic Health Record Data

Venous thromboembolism (VTE) is a leading cause of preventable inpatient mortality, while the real-world performance of mandated risk assessment and t...

A Vision-Language Model for Coronary Angiography Interpretation and Clinical Decision Support

BACKGROUND: Coronary angiography remains the reference standard for diagnosing coronary artery disease and guiding revascularization, yet its interpre...

Agentic Artificial Intelligence as a Catalyst for Administrative Modernization: The Beginning of the End for Traditional Fax Workflows in Healthcare

Background: Healthcare has witnessed administrative staffing roles balloon to twice the number of employed clinicians, resulting in $950 billion per y...

Validating Artificial Intelligence Guidance for Ultrasound Acquisition and Remote Interpretation

Background: Venous thromboembolism (VTE), including deep vein thrombosis (DVT), remains a major global health burden. Diagnostic pathways rely on ultr...

Automated Phenotypic Characterization in Rare Hematologic Malignancies Using a Large Language Model-Based Framework

Background. Diagnosis and risk stratification in rare hematologic malignancies such as myeloproliferative neoplasms (MPNs) - polycythemia vera (PV), e...

Multi-omics and network propagation reveal latent innate immune programmes stratifying high-risk thrombotic primary antiphospholipid syndrome

Thrombotic primary antiphospholipid syndrome (thrPAPS) outcomes are associated with thrombosis type (arterial versus venous), recurrence, and antiphos...

CEVAR: Centerline Embedding Extraction for Endovascular Aneurysm Repair

Long-term mortality rates after endovascular aneurysm repair (EVAR) remain elevated due to post-EVAR rupture caused by loss of seal in stent graft sea...

Non-Invasive Arterial Blood Pressure Waveform Generation in Critically Ill Patients: A Sensor-Based Deep Learning Approach

Continuous monitoring of Arterial Blood Pressure (ABP) in critically ill patients requires invasive arterial catheterization, which carries risks of t...

abx_amr_simulator: A simulation environment for antibiotic prescribing policy optimization under antimicrobial resistance

Antimicrobial resistance (AMR) poses a global health threat, reducing the effectiveness of antibiotics and complicating clinical decision-making. To a...

Automated high-throughput fabrication of patient-specific vessel-on-chips enables a generative AI digital twin--Cascade Learner of Thrombosis (CLoT) for personalized thrombosis prediction

We developed an integrated platform combining high-throughput automated biofabrication, systematic patient-derived tissue experiments, and specialized...

Systems Biology and Machine Learning Decode an Immunometabolic Signature for Post-Thrombotic Syndrome

Objective: Post-thrombotic syndrome (PTS), a common complication of deep vein thrombosis, lacks objective diagnostic biomarkers and its molecular mech...

Predicting the risk of in-stent restenosis after drug-eluting stent implantation: Pipe dream or machine learning-enabled opportunity?

MACHINE LEARNING AND BIOINFORMATICS TO IDENTIFY COAGULATION BIOMARKERS IN SEPSIS-RELATED KIDNEY INJURY.

Background: Sepsis-associated acute kidney injury (SA-AKI) is a life-threatening complication with mortality rates exceeding 50%, yet its molecular dr...

Advanced Deep Learning Techniques for Automated Segmentation of Type B Aortic Dissections

Purpose: Aortic dissections are life-threatening cardiovascular conditions requiring accurate segmentation of true lumen (TL), false lumen (FL), and...

DWI-based deep learning radiomics nomogram for predicting the impaired quality of life in patients with unruptured intracranial aneurysm developing new iatrogenic cerebral infarcts following stent placement: a multicenter cohort study.

This study developed a DWI-based radiomics nomogram to predict impaired health-related quality of life (HRQOL) in patients with unruptured intracrania...

Fast Virtual Stenting for Thoracic Endovascular Aortic Repair of Aortic Dissection Using Graph Deep Learning.

Fast virtual stenting (FVS) is a promising preoperative planning aid for thoracic endovascular aortic repair (TEVAR) of aortic dissection. It aims at ...

Machine learning models based on a national-scale cohort accurately identify patients at high risk of deep vein thrombosis following primary total hip arthroplasty.

BACKGROUND: The occurrence of deep venous thrombosis (DVT) following total hip arthroplasty (THA) poses a substantial risk of morbidity and mortality,...