Critical Care

Enhancing mechanical ventilator reliability through machine learning based predictive maintenance.

BackgroundWith the advancement of Artificial Intelligence (AI), clinical engineering has witnessed transformative opportunities, enabling predictive maintenance of medical devices, optimization of healthcare workflows, and personalized patient care. Respiratory equipment plays a vital role in modern healthcare, supporting patients with compromised or impaired respiratory capacities. However, ensur...

Learning Multi-view Multi-class Anomaly Detection

The latest trend in anomaly detection is to train a unified model instead of training a separate model for each category. However, existing multi-class anomaly detection (MCAD) models perform poorly in multi-view scenarios because they often fail to effectively model the relationships and complementary information among different views. In this paper, we introduce a Multi-View Multi-Class Anomal...

Mamba Based Feature Extraction And Adaptive Multilevel Feature Fusion For 3D Tumor Segmentation From Multi-modal Medical Image

Multi-modal 3D medical image segmentation aims to accurately identify tumor regions across different modalities, facing challenges from variations i...

Pediatric Asthma Detection with Googles HeAR Model: An AI-Driven Respiratory Sound Classifier

Early detection of asthma in children is crucial to prevent long-term respiratory complications and reduce emergency interventions. This work presen...

Breast Cancer Detection from Multi-View Screening Mammograms with Visual Prompt Tuning

Accurate detection of breast cancer from high-resolution mammograms is crucial for early diagnosis and effective treatment planning. Previous studie...

DiVE: Efficient Multi-View Driving Scenes Generation Based on Video Diffusion Transformer

Collecting multi-view driving scenario videos to enhance the performance of 3D visual perception tasks presents significant challenges and incurs su...

Model uncertainty quantification using feature confidence sets for outcome excursions

When implementing prediction models for high-stakes real-world applications such as medicine, finance, and autonomous systems, quantifying predictio...

Heterogeneous network drug-target interaction prediction model based on graph wavelet transform and multi-level contrastive learning

Drug-target interaction (DTI) prediction is a core task in drug development and precision medicine in the biomedical field. However, traditional mac...

Assessing the Utility of Audio Foundation Models for Heart and Respiratory Sound Analysis

Pre-trained deep learning models, known as foundation models, have become essential building blocks in machine learning domains such as natural lang...

Severity Classification of Chronic Obstructive Pulmonary Disease in Intensive Care Units: A Semi-Supervised Approach Using MIMIC-III Dataset

Chronic obstructive pulmonary disease (COPD) represents a significant global health burden, where precise severity assessment is particularly critic...

Early Detection of Multidrug Resistance Using Multivariate Time Series Analysis and Interpretable Patient-Similarity Representations

Background and Objectives: Multidrug Resistance (MDR) is a critical global health issue, causing increased hospital stays, healthcare costs, and mor...

An introduction to R package `mvs`

In biomedical science, a set of objects or persons can often be described by multiple distinct sets of features obtained from different data sources...

ExOSITO: Explainable Off-Policy Learning with Side Information for Intensive Care Unit Blood Test Orders

Ordering a minimal subset of lab tests for patients in the intensive care unit (ICU) can be challenging. Care teams must balance between ensuring th...

Waveform-Logmel Audio Neural Networks for Respiratory Sound Classification

Auscultatory analysis using an electronic stethoscope has attracted increasing attention in the clinical diagnosis of respiratory diseases. Recently...

Balancing Fairness and Performance in Healthcare AI: A Gradient Reconciliation Approach

The rapid growth of healthcare data and advances in computational power have accelerated the adoption of artificial intelligence (AI) in medicine. H...

Study on the mechanism of action of the active ingredient of Calculus Bovis in the treatment of sepsis by integrating single-cell sequencing and machine learning.

BACKGROUND: Sepsis, a complex inflammatory condition with high mortality rates, lacks effective treatments. This study explores the therapeutic mechan...

EarthGPT-X: Enabling MLLMs to Flexibly and Comprehensively Understand Multi-Source Remote Sensing Imagery

Recent advances in the visual-language area have developed natural multi-modal large language models (MLLMs) for spatial reasoning through visual pr...

GPMFS: Global Foundation and Personalized Optimization for Multi-Label Feature Selection

As artificial intelligence methods are increasingly applied to complex task scenarios, high dimensional multi-label learning has emerged as a promin...

TUMLS: Trustful Fully Unsupervised Multi-Level Segmentation for Whole Slide Images of Histology

Digital pathology, augmented by artificial intelligence (AI), holds significant promise for improving the workflow of pathologists. However, challen...

Predictive Multiplicity in Survival Models: A Method for Quantifying Model Uncertainty in Predictive Maintenance Applications

In many applications, especially those involving prediction, models may yield near-optimal performance yet significantly disagree on individual-leve...