Hospital-Based Medicine

Applying machine learning and natural language processing to patient safety event reports: Identifying patterns of cardiovascular diagnostic errors.

OBJECTIVE: To explore the utility of natural language processing (NLP) and machine learning (ML) techniques to identify unsafe conditions leading to cardiovascular diagnostic errors using patient safety event (PSE) reports data. METHODS: PSE reports from January 2016 to August 2021 from a multi-hospital healthcare system in the mid-Atlantic region of the United States were included in this study. ...

Research progress in flap temperature monitoring technologies and applications after transplantation.

As a primary method for tissue repair and functional reconstruction, flap transplantation has achieved a clinical success rate of over 90%. However, the postoperative incidence of vascular crisis remains as high as 10%-30%, making it a leading cause of reoperation and disability. Flap temperature is a key indicator closely correlated with microcirculatory status, making its monitoring essential fo...

'Malnutrition Receives High Priority': Nurses' Experiences in Providing Nutritional Support in Hospitalized Patients-A Qualitative Study.

AIMS: To describe nurses' experiences in managing malnutrition in hospitalized adults and providing support along an interprofessional nutritional sta...

Enhancing Community-Based Nursing Decision Support: Machine Learning Models for Diabetes Risk Prediction Using Home Health Nursing Notes.

OBJECTIVES: This study aimed to identify high-risk factors for type 2 diabetes and develop a machine learning (ML)-based diabetes prediction model usi...

An Emerging Role of Artificial Intelligence in Pediatric Neuroanesthesia.

Anesthesia for pediatric neurosurgery represents a highly complex and challenging field, characterized by age-dependent physiological variability, het...

Innovations in Deaf Health Care Communication: Systematic Review of Sign Language Recognition Systems.

BACKGROUND: Deaf individuals often face communication challenges when interacting with those who can hear. Within health care settings, these challeng...

Direct Annotation of Unstructured Clinical Progress Notes Describes Postpartum Healthcare Needs and Encounter Characteristics of Women Whose Newborns Are Immediately Hospitalized in Intensive Care.

BACKGROUND: Among children's hospitals with integrated high-risk prenatal care and birthing facilities, the reasons for postpartum care-seeking, and d...

A dual-index framework for cold atmospheric plasma dosing: A meta-analysis of parametric control from volumetric redox flux to redox homeostatic status.

Cold atmospheric plasma (CAP) has emerged as a versatile therapeutic platform with demonstrated efficacy across diverse disease models. Despite signif...

The relationship between mental health disorders and LUTS.

This narrative review explores the complex relationship between lower urinary tract symptoms (LUTS) and mental health disorders, focusing on the influ...

Artificial intelligence in emergency nursing: A scoping review of applications and implications.

INTRODUCTION: Artificial intelligence (AI) has advanced rapidly in healthcare; however, its application in emergency nursing remains underexplored. Th...

Exploring the Role of AI in Managing Treatment Recommendations for Lymphedema: International, Multidisciplinary, Multiprofessional Survey Study of Trust, Reliability, and Impact on Decision-Making.

BACKGROUND: Upper and lower extremity lymphedema is a chronic, progressive condition that significantly impairs the quality of life of affected patien...

Analyzing the deep learning approach-based modeling framework to understand the critical environmental factors of predicting daily nitrate concentrations.

Continuous stream nitrate data is critical for understanding nutrient dynamics and informing management practices. Recent studies applied deep learnin...

Innovation in intensive care: a framework to turn ideas and concepts into actionable solutions.

INTRODUCTION: Intensive care is under increasing pressure due to demographic changes, availability of new complex therapies, and workforce shortages, ...

Interpretable machine learning models for stroke risk prediction in patients with newly diagnosed atrial fibrillation.

Atrial fibrillation (AF) is the most common sustained arrhythmia and a leading cause of ischemic stroke. Existing risk scores, such as CHA₂DS₂-VASc, o...

Decision-making for ICU admission: is there a place for AI? Exploring and understanding meaning, experiences, and perspectives.

A multimodal predictive model incorporating transcriptomic-guided blood biomarkers and clinical variables for sepsis-associated acute kidney injury.

Sepsis-associated acute kidney injury (SA-AKI) is a major complication in the intensive care unit (ICU), and early risk stratification remains challen...

The CAMUS Initiative: A Multiphase, Multicentre International Collaboration to Redefine Risk Stratification, Reporting, and Grading of Surgical Complications in Urology.

Surgical complications remain a major source of preventable morbidity, mortality, and health care expenditure, but existing frameworks such as the Cla...

Voice Biomarkers: Cognitive Impairment, including Alzheimer's Disease, Dementia, or Mild Cognitive Impairment: Introduction to Peripheral Neuropathy.

INTRODUCTION/OBJECTIVE: Alzheimer's disease (AD) can cause certain nervous disorders, which in turn can lead to voice disorders and abnormal values fo...

Integrated Strategies for Nursing and Rehabilitation in Lung Cancer Patients.

This review delineates the pivotal role of nursing and rehabilitation in perioperative management and chemotherapy support for lung cancer patients, w...

Dynamic trajectories of organ dysfunction in sepsis using the SOFA-2 score and early prediction from multicenter cohorts.

OBJECTIVES: The Sequential Organ Failure Assessment-2 (SOFA-2) score reflects contemporary intensive care practices, yet its longitudinal application ...