Critical Care

External Testing of a Deep Learning Model to Estimate Biologic Age Using Chest Radiographs.

Purpose To assess the prognostic value of a deep learning-based chest radiographic age (hereafter, CXR-Age) model in a large external test cohort of Asian individuals. Materials and Methods This single-center, retrospective study included chest radiographs from consecutive, asymptomatic Asian individuals aged 50-80 years who underwent health checkups between January 2004 and June 2018. This study ...

A tailored machine learning approach for mortality prediction in severe COVID-19 treated with glucocorticoids.

The impact of severe COVID-19 pneumonia on healthcare systems highlighted the need for accurate predictions to improve patient outcomes. Despite the established efficacy of glucocorticoids (GCs), variable patient responses are observed, and the existing clinical scores are limited in predicting non-responders. We propose a machine learning (ML) based approach to predi...

Explainable Artificial Intelligence for Early Prediction of Pressure Injury Risk.

BACKGROUND: Hospital-acquired pressure injuries (HAPIs) have a major impact on patient outcomes in intensive care units (ICUs). Effective prevention r...

Multi-scale Multi-instance Visual Sound Localization and Segmentation

Visual sound localization is a typical and challenging problem that predicts the location of objects corresponding to the sound source in a video. P...

Large-Scale Multi-omic Biosequence Transformers for Modeling Protein-Nucleic Acid Interactions

The transformer architecture has revolutionized bioinformatics and driven progress in the understanding and prediction of the properties of biomolec...

Towards reliable respiratory disease diagnosis based on cough sounds and vision transformers

Recent advancements in deep learning techniques have sparked performance boosts in various real-world applications including disease diagnosis based...

DrugAgent: Multi-Agent Large Language Model-Based Reasoning for Drug-Target Interaction Prediction

Advancements in large language models (LLMs) allow them to address diverse questions using human-like interfaces. Still, limitations in their traini...

User-Centered Development of Explanation User Interfaces for AI-Based CDSS: Lessons Learned from Early Phases.

This paper reports lessons learned during the early phases of the user-centered design process for an explanation user interface for an AI-based clini...

Machine Learning to Predict the Risk of Malnutrition in Hospitalized Patients with Pneumonia and Analysis of Related Prognostic Factor.

This study explored machine learning's potential in predicting the nutritional status and outcomes for pneumonia patients. It focused on 4,368 patient...

A Comparative Analysis of Federated and Centralized Learning for SpO2 Prediction in Five Critical Care Databases.

This study explores the potential of federated learning (FL) to develop a predictive model of hypoxemia in intensive care unit (ICU) patients. Central...

Multi-Objective Performance Optimization of Machine Learning Models in Healthcare.

Multi-objective optimization holds particular significance for medical applications, wherein enhancing sensitivity is crucial to avoid costly missed d...

Optimizing ICU Care: Machine Learning and PCA for Early Prediction of Renal Replacement Therapy Requirement.

Forecasting the need for Renal Replacement Therapy (RRT) in intensive care units (ICUs) at an early stage can enhance patient outcomes and optimize re...

Development and Validation of an Interpretable Machine Learning Model for Early Prognosis Prediction in ICU Patients with Malignant Tumors and Hyperkalemia.

This study aims to develop and validate a machine learning (ML) predictive model for assessing mortality in patients with malignant tumors and hyperka...

SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing

Sepsis is the leading cause of in-hospital mortality in the USA. Early sepsis onset prediction and diagnosis could significantly improve the surviva...

Machine Learning-Based Prediction Models of Mortality for Intensive Care Unit Patients Using Nursing Records.

This study aimed to develop ICU mortality prediction models using a conceptual framework, focusing on nurses' concerns reflected in nursing records fr...

Development of a Mapping Table for Nursing Notes Based on Nurses' Concerns in ICU Patients.

This study aimed to develop a mapping table that connects nursing notes with standard terminology, focusing on nurses' concerns for ICU patients. Afte...

PsyDI: Towards a Personalized and Progressively In-depth Chatbot for Psychological Measurements

In the field of psychology, traditional assessment methods, such as standardized scales, are frequently critiqued for their static nature, lack of p...

Improving Prediction of Need for Mechanical Ventilation using Cross-Attention

In the intensive care unit, the capability to predict the need for mechanical ventilation (MV) facilitates more timely interventions to improve pati...

Mapping Patient Trajectories: Understanding and Visualizing Sepsis Prognostic Pathways from Patients Clinical Narratives

In recent years, healthcare professionals are increasingly emphasizing on personalized and evidence-based patient care through the exploration of pr...

Identification and validation of potential genes for the diagnosis of sepsis by bioinformatics and 2-sample Mendelian randomization study.

This integrated study combines bioinformatics, machine learning, and Mendelian randomization (MR) to discover and validate molecular biomarkers for se...