Nursing

Rapid diagnosis of fever etiology using wearable temperature monitoring and machine learning

Introduction. Distinct temperature patterns have long been recognized to correlate with fevers of differing etiologies. While the use of wearable sensors for high-frequency temperature monitoring (HFTM) on a near minute-by-minute basis has been shown to detect fevers earlier than standard-of-care nursing vital sign assessments in hospitalized patients, leveraging these high-resolution datasets to ...

Forecasting Trajectories of Physiological Mechanics with Sparse Clinical Data Using a Data Assimilation and Machine Learning Hybrid

Clinical decisions for determining optimal patient-specific interventions are complicated prediction tasks that rely on health care professionals' understanding of physiological mechanisms and their dynamics. These decisions are challenged by (a) observational data sparsity and (b) patient heterogeneity. Here, we focus on estimating and forecasting specific physiological properties--that are not e...

From CHESS to CHECKMATE: A Practical Score for Predicting Shunt Dependency Following Subarachnoid Hemorrhage

Objective: Shunt-dependent hydrocephalus is a common and costly complication of aneurysmal subarachnoid hemorrhage (aSAH), affecting up to 28% of surv...

Hierarchy-Aware and Anatomy-Guided Learning for Lung Ultrasound Video Classification

Lung ultrasound (LUS) is a bedside tool for assessing pulmonary edema in patients at risk due to heart failure or impaired kidney function. However, a...

How Do Nurses Make Clinical Decisions Via Remote Reviews: A Convergent Mixed-Methods Study

Abstract Background: Remote clinical reviews have become an integral component of contemporary nursing practice across community and acute care settin...

Protocol for an EHR-embedded pragmatic randomized control trial of Ambient AI to Reduce Nursing Staff Documentation Time

Background: Documentation burden significantly impacts nursing workload and well-being, with nurses spending an estimated 20-40% of their time on docu...

LoRA-Based Cascaded Multimodal Fusion for Action Recognition in Medical Training Environments

This paper presents a cascaded Low-Rank Adaptation (LoRA)-based multimodal fusion framework for action and activity recognition in healthcare-oriented...

How Best to Explain Machine Learning Models to Clinicians: A User Study of Explanation Types

Background: Explanations play a crucial role in helping clinicians understand how black-box machine learning models make predictions in clinical setti...

Overview of the NLPCC 2026 Shared Task 1: Difficulty-Aware Multilingual and Multimodal Medical Instructional Video Understanding Evaluation

Following the CMIVQA, MMI-VQA, and M4IVQA challenges in NLPCC 2023--2025, we introduce the Difficulty-Aware Medical Instructional Video Question Answe...

Managing AI-Enabled Uncertainty in Clinical AI Deployment: Mixed-Methods Study of Governance, Workflow, and Organizational Learning in an ICU Decision Support Pilot

BackgroundHealth care organizations are increasingly required to make strategic decisions about artificial intelligence (AI) systems before their clin...

Aloe-Vision: Robust Vision-Language Models for Healthcare

Large Vision-Language Models (LVLMs) specialized in healthcare are emerging as a promising research direction due to their potential impact in clinica...

A Natural Experiment Reveals Clinically Essential and Compliance-Driven Nursing Documentation

Despite contributing substantially to clinician burnout, nursing documentation lacks empirical evidence distinguishing clinically essential from admin...

Automated EEG Classification to Track Levels of Consciousness

Precise prognostication in acute brain injury is limited by a lack of reliable biomarkers of consciousness available to clinicians at the bedside. The...

Generative Artificial Intelligence in Psychotherapy Practice: A Global Online Survey of Mental Health Professionals' Adoption

Background: Generative artificial intelligence (GenAI) tools, including large language model (LLM)-based platforms such as ChatGPT, Google Gemini, and...

Deep learning-based detection of cessation of breathing in pre-term infants

Apnoea of prematurity is characterised by recurrent episodes of cessation of breathing and remains difficult to detect reliably using routinely monito...

Ranking-optimized survival models can underperform fixed-horizon clinical prediction: a SUPPORT2 reanalysis of machine learning, attending-physician judgment, and the original SUPPORT model at 60- and 180-day mortality

Machine-learning survival models are increasingly proposed for intensive-care mortality prediction and are almost always selected and reported using t...

Non-invasive intracranial pressure waveform reconstruction with deep learning

Purpose: Continuous intracranial pressure (ICP) monitoring requires invasive instrumentation, reaching only a narrow subset of critically ill patients...

Semantic Embeddings and the Peripheral Transcriptome in Ischemic Stroke: Connecting Molecular Signatures to NANDA-I Diagnoses

Objective: To construct and evaluate, in an exploratory manner, a pathophysiologic rationale link- ing biological pathways derived from the peripheral...

A Machine Learning Pipeline for Scalable Annotation of Patient-Ventilator Dyssynchrony from Bedside Ventilator Data

Objective: Patient-ventilator dyssynchrony (PVD) is a common and clinically consequential problem in critically ill patients receiving invasive mechan...

Pre-AF 13: An Interpretable Atrial Fibrillation Risk Score Mined from Discharge Reports

Background. Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia and a major determinant of prognosis. Established AF risk scores rely on...