Prevention of medical errors

Selective Attention Federated Learning: Improving Privacy and Efficiency for Clinical Text Classification

Federated Learning (FL) faces major challenges regarding communication overhead and model privacy when training large language models (LLMs), especially in healthcare applications. To address these, we introduce Selective Attention Federated Learning (SAFL), a novel approach that dynamically fine-tunes only those transformer layers identified as attention-critical. By employing attention pattern...

Large Language Models for Drug Overdose Prediction from Longitudinal Medical Records

The ability to predict drug overdose risk from a patient's medical records is crucial for timely intervention and prevention. Traditional machine learning models have shown promise in analyzing longitudinal medical records for this task. However, recent advancements in large language models (LLMs) offer an opportunity to enhance prediction performance by leveraging their ability to process long ...

A New Paradigm of User-Centric Wireless Communication Driven by Large Language Models

The next generation of wireless communications seeks to deeply integrate artificial intelligence (AI) with user-centric communication networks, with...

The Art of Audience Engagement: LLM-Based Thin-Slicing of Scientific Talks

This paper examines the thin-slicing approach - the ability to make accurate judgments based on minimal information - in the context of scientific p...

SilVar-Med: A Speech-Driven Visual Language Model for Explainable Abnormality Detection in Medical Imaging

Medical Visual Language Models have shown great potential in various healthcare applications, including medical image captioning and diagnostic assi...

Federated Prototype Graph Learning

In recent years, Federated Graph Learning (FGL) has gained significant attention for its distributed training capabilities in graph-based machine in...

Using Vision Language Models for Safety Hazard Identification in Construction

Safety hazard identification and prevention are the key elements of proactive safety management. Previous research has extensively explored the appl...

[Advancements in machine learning applications in refractive surgery].

Refractive error is a significant factor contributing to visual impairment, imposing a relatively large burden on the social economy. Although refract...

Focal Cortical Dysplasia Type II Detection Using Cross Modality Transfer Learning and Grad-CAM in 3D-CNNs for MRI Analysis

Focal cortical dysplasia (FCD) type II is a major cause of drug-resistant epilepsy, often curable only by surgery. Despite its clinical importance, ...

The Role of Machine Learning in Reducing Healthcare Costs: The Impact of Medication Adherence and Preventive Care on Hospitalization Expenses

This study reveals the important role of prevention care and medication adherence in reducing hospitalizations. By using a structured dataset of 1,1...

CiMBA: Accelerating Genome Sequencing through On-Device Basecalling via Compute-in-Memory

As genome sequencing is finding utility in a wide variety of domains beyond the confines of traditional medical settings, its computational pipeline...

DIMA: DIffusing Motion Artifacts for unsupervised correction in brain MRI images

Motion artifacts remain a significant challenge in Magnetic Resonance Imaging (MRI), compromising diagnostic quality and potentially leading to misd...

Parallel GPU-Enabled Algorithms for SpGEMM on Arbitrary Semirings with Hybrid Communication

Sparse General Matrix Multiply (SpGEMM) is key for various High-Performance Computing (HPC) applications such as genomics and graph analytics. Using...

Embedded Federated Feature Selection with Dynamic Sparse Training: Balancing Accuracy-Cost Tradeoffs

Federated Learning (FL) enables multiple resource-constrained edge devices with varying levels of heterogeneity to collaboratively train a global mo...

Federated Learning for Medical Image Classification: A Comprehensive Benchmark

The federated learning paradigm is wellsuited for the field of medical image analysis, as it can effectively cope with machine learning on isolated ...

Statistical Management of the False Discovery Rate in Medical Instance Segmentation Based on Conformal Risk Control

Instance segmentation plays a pivotal role in medical image analysis by enabling precise localization and delineation of lesions, tumors, and anatom...

Improving Early Prediction of Type 2 Diabetes Mellitus with ECG-DiaNet: A Multimodal Neural Network Leveraging Electrocardiogram and Clinical Risk Factors

Type 2 Diabetes Mellitus (T2DM) remains a global health challenge, underscoring the need for early and accurate risk prediction. This study presents...

MultiMed-ST: Large-scale Many-to-many Multilingual Medical Speech Translation

Multilingual speech translation (ST) in the medical domain enhances patient care by enabling efficient communication across language barriers, allev...

FAST: Federated Active Learning with Foundation Models for Communication-efficient Sampling and Training

Federated Active Learning (FAL) has emerged as a promising framework to leverage large quantities of unlabeled data across distributed clients while...

Bias in Large Language Models Across Clinical Applications: A Systematic Review

Background: Large language models (LLMs) are rapidly being integrated into healthcare, promising to enhance various clinical tasks. However, concern...