Infection Control

Aligning neonatal sepsis triage diagnostics with WHO target product profiles in low- and middle-income countries.

Neonatal sepsis is a leading cause of infant morbidity and mortality in low- and middle-income countries (LMICs), yet despite this, progress in the development and implementation of effective diagnostics has been limited. Current diagnostic methods rely on clinical assessments of nonspecific signs and symptoms, limited laboratory testing and technologies that are inaccessible, unaffordable or unsu...

Accurate surgery time prediction (ASTP) strategy based on artificial intelligence techniques.

Accurate timing prediction of surgery is essential for efficient operating room scheduling and ensuring patient care. This study proposes a two-layered Accurate Surgical Time Prediction (ASTP) framework. The first layer combines feature importance and advanced machine learning models to estimate surgical time. After preprocessing, two complementary interpretable AI methods were used: Long Short Te...

Radiomics-driven prediction of postoperative infection risk in maxillofacial fractures: multidimensional risk factor analysis and model construction based on absorbable plate fixation.

OBJECTIVES: This study proposes a predictive model that integrates radiological features with multidimensional clinical factors for the accurate predi...

Barriers and Facilitators to Physician-Patient Communication in Chinese Tertiary Hospitals From the Perspectives of Hospital-Based Physicians and Patient Relations Coordinators: Qualitative Study.

BACKGROUND: Effective physician-patient communication is essential for building trust and sustaining positive relationships, yet becomes increasingly ...

Explainable artificial intelligence to assess hospital financial performance during the COVID-19 pandemic through operating margin metric.

PURPOSE: This study addresses critical gaps in understanding hospital financial resilience during the COVID-19 pandemic by developing an AI framework ...

Exploratory Evaluation of Dual Digital Platforms for Acute Brain Conditions in Medically Underserved Rural Areas : A Chuncheon, Gangwon-do Experience.

OBJECTIVE: To describe an exploratory initial experience with two complementary digital platforms consisting of an AI-based telemedicine system and an...

Impact of the EILA application on smoking cessation among hospital workers: a randomised controlled trial.

OBJECTIVES: Smoking remains a major preventable cause of lung and cardiovascular disease. This randomised controlled trial evaluated EILA, a mobile ap...

A deep learning system for bacterial identification and resistance prediction from MALDI-TOF data.

Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) data contain underutilized information beyond species id...

Effects of cerebral infarction on cognitive and speech performance.

OBJECTIVE: The present study sought to identify the cognitive features and speech features that might distinguish cerebral infarction patients by usin...

Development, internal testing, and external validation of an interpretable machine-learning model for predicting imaging-confirmed in-hospital postoperative deep vein thrombosis in older patients with patellar fractures.

BACKGROUND: Older patients with patellar fractures may be at increased risk of postoperative deep vein thrombosis (DVT) because of trauma, perioperati...

Construction and validation of a machine learning-based prediction model for social isolation in patients with colorectal cancer after stoma surgery.

OBJECTIVE: To investigate the current status and influencing factors of social alienation in patients with enterostomy after colorectal cancer surgery...

Accurate prediction of mortality in children with sepsis: development and validation of an explainable model based on real-world data.

BACKGROUND: Sepsis remains the leading cause of in-hospital deaths among children, and there is currently a lack of precise early prediction models. T...

AI-Based Triage Decision Support: Multisite Economic Evaluation in the United States.

BACKGROUND: Emergency department (ED) visits have risen in the United States, with demand for emergency care exceeding supply. Resultant ED crowding h...

Novel Online Platform for Trauma Care-Integrating Trauma Phenotypes to Optimize the Trauma and Injury Severity Score Model: Retrospective Cohort Study.

BACKGROUND: Severe trauma remains a leading cause of admission to the intensive care unit. The Trauma and Injury Severity Score (TRISS) is an establis...

Missing-data-aware machine learning prediction of in-hospital major adverse cardiovascular events after primary percutaneous coronary intervention for ST-segment elevation myocardial infarction.

Machine learning (ML) offers opportunities to improve prognostication after ST-segment elevation myocardial infarction (STEMI), but real-world registr...

Feasibility and Preliminary Effectiveness of the ChulaCancer Mobile Chatbot for Supportive Care of Patients With Breast or Colorectal Cancer Receiving Chemotherapy: Pilot Randomized Controlled Trial.

BACKGROUND: Chemotherapy-related toxicities often lead to unscheduled health care use and diminished quality of life. Digital health interventions, su...

AI for predicting exacerbations in KIDs with asthma (AIRE-KIDS).

Recurrent acute care visits are a common yet preventable outcome for many children with asthma. Machine learning (ML) applied to electronic medical re...

Prevalence and Predictors of Non-Exclusive Breastfeeding at Hospital Discharge in Uruguay.

BACKGROUND: The prescription of infant formula during postpartum hospitalization is one of several factors that influence breastfeeding. RESEARCH AIMS...

Machine Learning-Based Prediction Model for 30-Day Emergency Department Revisits in a Medically Underserved Tertiary Hospital: Formative Retrospective Cohort Study.

BACKGROUND: Emergency department (ED) revisits are critical quality indicators, particularly in medically underserved areas, where traditional predict...

An integrated computational-experimental approach identifies Malate Synthase G as therapeutic target to disrupt carbon metabolism in chronic Pseudomonas aeruginosa infection.

Pseudomonas aeruginosa is a leading cause of nosocomial infections, particularly in individuals with a compromised immune system. Due to its strong ad...