Intensivists

GAMMNet: Gating Multi-head Attention in a Multi-modal Deep Network for Sound Based Respiratory Disease Detection.

Respiratory diseases present significant challenges to global health due to their high morbidity and mortality rates. Traditional diagnostic methods, such as chest radiographs and blood tests, often lead to unnecessary costs and resource strain, as well as potential risks of cross-contamination during these procedures. In recent years, contactless sensing and intelligent technologies, particularly...

Development, Validation, and Deployment of a Time-Dependent Machine Learning Model for Predicting One-Year Mortality Risk in Critically Ill Patients with Heart Failure.

Heart failure (HF) ranks among the foremost causes of mortality globally, exhibiting particularly high prevalence and significant impact within intensive care units (ICUs). This study sought to develop, validate, and deploy a time-dependent machine learning model aimed at predicting the one-year all-cause mortality risk in ICU patients diagnosed with HF, thereby facilitating precise prognostic ev...

Mitigating Bias in Machine Learning Models with Ethics-Based Initiatives: The Case of Sepsis.

This paper discusses ethics-based strategies for mitigating bias in machine learning models used to predict sepsis onset. The first part discusses how...

Overview of the NLPCC 2025 Shared Task 4: Multi-modal, Multilingual, and Multi-hop Medical Instructional Video Question Answering Challenge

Following the successful hosts of the 1-st (NLPCC 2023 Foshan) CMIVQA and the 2-rd (NLPCC 2024 Hangzhou) MMIVQA challenges, this year, a new task ha...

Prediction and optimization of stretch flangeability of advanced high strength steels utilizing machine learning approaches.

Advanced high strength steels (AHSS) exhibit diverse mechanical properties due to their complex chemical compositions and microstructures. Existing ma...

Machine learning model to predict sepsis in ICU patients with intracerebral hemorrhage.

Patients with intracerebral hemorrhage (ICH) are highly susceptible to sepsis. This study evaluates the efficacy of machine learning (ML) models in pr...

Are Wearable ECG Devices Ready for Hospital at Home Application?

The increasing focus on improving care for high-cost patients has highlighted the potential of Hospital at Home (HaH) and remote patient monitoring (R...

Machine-Learning-Powered, Rapid, Accurate, and Multi-Target Mechanical Metamaterials Inverse Design.

Multi-target inverse design, which involves designing multiple targets with different optimization objectives, becomes a key focus in mechanical metam...

Best practice in rheumatology new developments in ultrasound and MRI imaging of pediatric rheumatic diseases.

The evidence base for ultrasound and MRI imaging in pediatric rheumatic diseases continues to grow, enabling the routine clinical use of the two techn...

Enhancing LLMs' Clinical Reasoning with Real-World Data from a Nationwide Sepsis Registry

Although large language models (LLMs) have demonstrated impressive reasoning capabilities across general domains, their effectiveness in real-world ...

From Biometrics to Environmental Control: AI-Enhanced Digital Twins for Personalized Health Interventions in Healing Landscapes

The dynamic nature of human health and comfort calls for adaptive systems that respond to individual physiological needs in real time. This paper pr...

Probability Score for the Diagnosis of Periprosthetic Joint Infection: Development and Validation of a Practical Multi-analyte Machine Learning Model.

Background and objective The diagnosis of periprosthetic joint infection (PJI) relies on established criteria-based systems requiring interpretation a...

Generative AI mitigates representation bias and improves model fairness through synthetic health data.

Representation bias in health data can lead to unfair decisions and compromise the generalisability of research findings. As a consequence, underrepre...

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-cl...

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...

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...

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...

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...

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...