Devices and Vaccines

An AI-driven machine learning approach identifies risk factors associated with 30-day mortality following total aortic arch replacement combined with stent elephant implantation

During emergency surgery, patients with acute type A aortic dissection (ATAAD) experience unfavorable outcomes throughout their hospital stay. The combination of total aortic arch replacement (TAR) and frozen elephant trunk (FET) implantation has become a dependable choice for surgical treatment. The objective of this research was to utilize a machine learning technique based on artificial intelli...

Unmet Needs in Acute Hepatic Porphyria Diagnosis: A Comparative Big Data Analysis of an AI-based Human-in-the-Loop Screening Versus Standard of Care

Acute Hepatic Porphyria (AHP) is a rare genetic disease characterized by unpredictable life-threatening attacks. There is no reliable biochemical screening test for patients outside of an attack and diagnosis is delayed on average by 15 (!) years. AI screening systems can assist in detecting AHP patients, but validating such systems is challenging, due to the limited number of suspected candidates...

Biomedical Text Normalization through Generative Modeling

Around 80% of electronic health record (EHR) data consists of unstructured medical language text. The formatting of this text is often flexible and in...

NTDscope: A multi-contrast portable microscope for disease diagnosis

Accurate diagnostics are essential for disease control and elimination efforts. However, access to diagnostics for neglected tropical diseases (NTDs) ...

Predictive performance of wearable sensors for mortality risk in older adults: a model development and validation study

Many adults in high-income countries carry a device capable of measuring physical- activity behaviour. Thus, there is public health need to understand...

Automated Extraction of Mortality Information from Publicly Available Sources Using Language Models

Mortality is a critical variable in healthcare research, especially for evaluating medical product safety and effectiveness. However, inconsistencies ...

A simple feed forward neural network to predict the 2025 outbreak of measles in the USA

Measles is a highly contagious viral disease associated with a variety of severe complications. Since 1963, widespread usage of a highly effective vac...

Clinical translation of ultrasoft Fleuron™ probes for stable, high-density, and bidirectional brain interfaces

Building brain foundation models to capture the underpinning neural dynamics of human behavior requires large functional neural datasets for training,...

Comprehensive Cerebral Aneurysm Rupture Prediction: From Clustering to Deep Learning

Cerebral aneurysm is a silent yet prevalent condition that affects a substantial portion of the global population. Aneurysms can develop due to variou...

Benchmarking transformer-based models for medical record deidentification: A single centre, multi-specialty evaluation

Robust de-identification is necessary to preserve patient confidentiality and maintain public acceptance of electronic health record (EHR) research. M...

A Standard Framework for Converting Coronary Angiography Reports into Machine-Readable Format Using Large Language Models

Coronary angiography (CAG) reports contain many details about coronary anatomy, lesion characteristics, and interventional procedures. However, their ...

Mapping Inequities in Global Vaccine Sentiment Research

Negative public sentiment towards vaccination (PSV) poses significant challenges to the effectiveness of immunization programs, with dramatic effects ...

SBDH-Reader: an LLM-powered method for extracting social and behavioral determinants of health from clinical notes

Social and behavioral determinants of health (SBDH) are increasingly recognized as essential for prognostication and informing targeted interventions....

Linking Trials to Publications: Enhancing Recall by Identifying Trial Registry Mentions in Full-Text

We have developed a free, public web-based tool, Trials to Publications, https://arrowsmith.psych.uic.edu/cgi-bin/arrowsmith_uic/TrialPubLinking/trial...

Deep learning-based localization of bounded edentulous spaces in intraoral occlusal images

This study developed a deep learning pipeline to localize bounded edentulous spaces with missing teeth FDI number directly from intraoral photographs,...

Diagnostic accuracy of a high-throughput multiplex immunoassay for the detection of Mpox virus infection and MVA-BN vaccination up to two years after exposure

Mpox, caused by mpox virus (MPXV), has gained global attention following the 2022 Clade IIb outbreak and the emergence of two novel Clade I lineages i...

The miniECG: Enabling interpretable detection of amplitude and intraventricular conduction ECG-abnormalities with a novel ECG device

The miniECG, a smartphone-sized, multi-lead device, offers a simple and fast alternative to the 12-lead ECG. We aimed to demonstrate the potential of ...

Design and Implementation of an End-to-End AI-Driven Colonoscopy Recall Workflow at Scale

We present a real-world deployment of a large language model-powered colonoscopy recall pipeline that structured over 100,000 patient records during a...

Preserving Privacy, Increasing Accessibility, and Reducing Cost: An On-Device Artificial Intelligence Model for Medical Transcription and Note Generation

Clinical documentation represents a significant burden for healthcare providers, with physicians spending up to 2 hours daily on administrative tasks....

CausalDRIFT: Causal Dimensionality Reduction via Inference of Feature Treatments for Robust Healthcare Machine Learning

High-dimensional medical datasets present challenges in feature selection, where traditional methods often prioritize spurious correlations over causa...