Infection Control

Machine learning for risk stratification of postoperative corneal edema in East Asian cataract patients: a model for precision ophthalmology.

PURPOSE: To develop and validate machine learning (ML) models for predicting early postoperative corneal edema (CE) after phacoemulsification in patients with normal preoperative corneal endothelium. METHODS: A retrospective cohort study analyzed data from 1128 eyes undergoing uncomplicated phacoemulsification at Tianjin Medical University Eye Hospital (May 2024-May 2025). CE was diagnosed on post...

Machine learning models for predicting readmission after stroke: A systematic review and meta-analysis.

BACKGROUND: Hospital readmission following stroke poses a significant challenge for healthcare systems. Machine learning (ML) offers the potential to improve prediction models for readmission risk, surpassing traditional statistical methods. However, the performance of ML models in such context has not been systematically evaluated. We aim to evaluate the performance of ML models in predicting pos...

Interpretable machine learning for predicting in-hospital mortality in COPD ICU patients: a rigorous validation across time and geography.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a common reason for admission to the intensive care unit (ICU), where accurate risk strati...

Development and external validation of an interpretable early prediction model for acute kidney injury using TabPFN and routine admission data: a retrospective cohort study.

BACKGROUND: Acute kidney injury (AKI) is a common and serious complication among hospitalized patients, and early risk stratification remains challeng...

Artificial intelligence for predicting hospital admissions from the emergency department: a prospective, quasi-experimental study.

The use of certain artificial intelligence (AI) tools may improve hospital operational efficiency, in particular in overcrowded emergency departments ...

The association between plasma IgG N-glycosylation and viral encephalitis in children: a hospital-based case-control study.

BACKGROUND: Viral encephalitis (VE) is an acute inflammatory disease caused by viral infection. Children are at a significantly higher risk of develop...

Superlearner can predict in-hospital mortality risk in critically ill patients with ischemic stroke: development and international validation.

BACKGROUND: Critically ill patients with ischemic stroke face substantial in-hospital mortality. Early and accurate prediction of mortality risk may f...

Deep learning models for acute kidney injury prediction: multi-center external validation and evaluation under simulated continuous monitoring conditions.

Acute kidney injury (AKI) is a common hospital complication with substantial morbidity and mortality. Deep learning models for AKI prediction show str...

Forecasting hospital bed occupancy: a time series approach with prophet.

BACKGROUND: Accurate hospital bed occupancy forecasting is essential for effective resource planning and patient flow management. While complex machin...

AI in practice: trends in hospital and clinic libraries.

The rise of artificial intelligence (AI) has introduced new possibilities for hospital and clinic libraries. A research project surveyed hospital and ...

A step-by-step guide for implementation of virtual surgical applications and patient data centralization: practical experience and lessons learned.

BACKGROUND: The expansion of digitalization in the pre-, intra- and post-operative surgical phases allow the development and integration of advanced t...

Automatic Extraction of PET RANO Criteria with an Externally Validated Deep Learning Model: Application to [18F]FDOPA PET Imaging.

BACKGROUND: Automatic segmentation of gliomas on amino acid PET is essential for quantitative tumor assessment, a pillar in monitoring gliomas under t...

Beyond NIHSS and neuroimaging: an interpretable gradient boosting model for predicting in-hospital mortality in ICU patients with acute ischemic stroke.

This study aimed to develop an interpretable machine learning model for predicting in-hospital mortality among acute ischemic stroke (AIS) patients ad...

Investigating the Implementation and Impact of AI-Assisted Fall Prevention in Hospitals: Protocol for a Multicenter, Multimethod Observational Study in Sweden (SAFE).

BACKGROUND: Artificial intelligence (AI) has the potential to enhance patient safety, particularly in the prevention of in-hospital falls. Recent adva...

Enhancing patient education in cataract surgery using a conversational artificial intelligence chatbot: pilot randomized controlled trial.

PURPOSE: To evaluate the effectiveness and usability of a safety-first, clinician-validated conversational artificial intelligence (AI) chatbot for ca...

AI for Hospital Infection Prevention and Control: Real-World Implementation, Impact, and the Gap Beyond Model Development.

BACKGROUND: Artificial intelligence and machine learning (AI/ML) may strengthen hospital infection prevention and control (IPC) through automated surv...

Perspectives from healthcare providers and decision-makers on strategies toward a future hospital: qualitative insights from a group modelling process.

AIM: Planning for a hospital is a complex and dynamic process, traditionally not informed by evidence. A systems thinking approach can be useful in in...

Concordance Analysis Between KDIGO Definition of Acute Kidney Injury and Its Coding in Clinical Practice.

RATIONALE & OBJECTIVE: Acute kidney injury (AKI) in research is typically identified using KDIGO criteria based on changes in serum creatinine (SCr) l...

Machine Learning Prediction of Hospital Stay in Pediatric Typhoid Intestinal Perforation: Pilot Study.

INTRODUCTION: Typhoid intestinal perforation (TIP) remains a significant cause of pediatric morbidity in resource-limited settings, with prolonged hos...

Antibiotic resistance prediction with an attention-based bi-LSTM clinical decision support system.

BACKGROUND AND OBJECTIVE: Antimicrobial resistance is recognized by the World Health Organization as a significant global health threat. In clinical p...