Infectious Disease

NeuroSep-CP-LCB: A Deep Learning-based Contextual Multi-armed Bandit Algorithm with Uncertainty Quantification for Early Sepsis Prediction

In critical care settings, timely and accurate predictions can significantly impact patient outcomes, especially for conditions like sepsis, where early intervention is crucial. We aim to model patient-specific reward functions in a contextual multi-armed bandit setting. The goal is to leverage patient-specific clinical features to optimize decision-making under uncertainty. This paper proposes ...

Sepsyn-OLCP: An Online Learning-based Framework for Early Sepsis Prediction with Uncertainty Quantification using Conformal Prediction

Sepsis is a life-threatening syndrome with high morbidity and mortality in hospitals. Early prediction of sepsis plays a crucial role in facilitating early interventions for septic patients. However, early sepsis prediction systems with uncertainty quantification and adaptive learning are scarce. This paper proposes Sepsyn-OLCP, a novel online learning algorithm for early sepsis prediction by in...

MMR: A Large-scale Benchmark Dataset for Multi-target and Multi-granularity Reasoning Segmentation

The fusion of Large Language Models with vision models is pioneering new possibilities in user-interactive vision-language tasks. A notable applicat...

AI-Driven Rapid Identification of Bacterial and Fungal Pathogens in Blood Smears of Septic Patients

Sepsis is a life-threatening condition which requires rapid diagnosis and treatment. Traditional microbiological methods are time-consuming and expe...

Vision-Language Models for Acute Tuberculosis Diagnosis: A Multimodal Approach Combining Imaging and Clinical Data

Background: This study introduces a Vision-Language Model (VLM) leveraging SIGLIP and Gemma-3b architectures for automated acute tuberculosis (TB) s...

Advancing Chronic Tuberculosis Diagnostics Using Vision-Language Models: A Multi modal Framework for Precision Analysis

Background: This study proposes a Vision-Language Model (VLM) leveraging the SIGLIP encoder and Gemma-3b transformer decoder to enhance automated ch...

Graph Discovery and Source Detection in Temporal Graphs

Researchers, policy makers, and engineers need to make sense of data on spreading processes as diverse as viral infections, water contamination, and...

Movement Sequencing: A Novel Approach to Quantifying the Building Blocks of Human Gait

By 2050, a quarter of the US population will be over the age of 65 with greater than a 40% risk of developing life-altering neuromusculoskeletal pat...

Artificial Intelligence-Driven Prognostic Classification of COVID-19 Using Chest X-rays: A Deep Learning Approach

Background: The COVID-19 pandemic has overwhelmed healthcare systems, emphasizing the need for AI-driven tools to assist in rapid and accurate patie...

Epidemic Forecasting with a Hybrid Deep Learning Method Using CNN-LSTM With WOA-GWO Parameter Optimization: Global COVID-19 Case Study

Effective epidemic modeling is essential for managing public health crises, requiring robust methods to predict disease spread and optimize resource...

COVID 19 Diagnosis Analysis using Transfer Learning

Coronaviruses, including SARS-CoV-2, are responsible for COVID-19, a highly transmissible disease that emerged in December 2019 in Wuhan, China. Dur...

DCAT: Dual Cross-Attention Fusion for Disease Classification in Radiological Images with Uncertainty Estimation

Accurate and reliable image classification is crucial in radiology, where diagnostic decisions significantly impact patient outcomes. Conventional d...

Systematic Classification of Studies Investigating Social Media Conversations about Long COVID Using a Novel Zero-Shot Transformer Framework

Long COVID continues to challenge public health by affecting a considerable number of individuals who have recovered from acute SARS-CoV-2 infection...

DynRsl-VLM: Enhancing Autonomous Driving Perception with Dynamic Resolution Vision-Language Models

Visual Question Answering (VQA) models, which fall under the category of vision-language models, conventionally execute multiple downsampling proces...

Learning transition path and membrane topological signatures in the folding pathway of bacteriorhodopsin (BR) fragment with artificial intelligence.

Membrane protein folding in the viscous microenvironment of a lipid bilayer is an inherently slow process that challenges experiments and computationa...

TAIJI: Textual Anchoring for Immunizing Jailbreak Images in Vision Language Models

Vision Language Models (VLMs) have demonstrated impressive inference capabilities, but remain vulnerable to jailbreak attacks that can induce harmfu...

Using Causal Inference to Explore Government Policy Impact on Computer Usage

We explore the causal relationship between COVID-19 lockdown policies and changes in personal computer usage. In particular, we examine how lockdown...

VaxGuard: A Multi-Generator, Multi-Type, and Multi-Role Dataset for Detecting LLM-Generated Vaccine Misinformation

Recent advancements in Large Language Models (LLMs) have significantly improved text generation capabilities. However, they also present challenges,...

Regulatory DNA sequence Design with Reinforcement Learning

Cis-regulatory elements (CREs), such as promoters and enhancers, are relatively short DNA sequences that directly regulate gene expression. The fitn...

SHAP-Integrated Convolutional Diagnostic Networks for Feature-Selective Medical Analysis

This study introduces the SHAP-integrated convolutional diagnostic network (SICDN), an interpretable feature selection method designed for limited d...