Infection Control

Deep learning model for pathological invasiveness prediction using smartphone-based surgical resection images in clinical stage IA lung adenocarcinoma (SuRImage): a prospective, multicentric, diagnostic study.

BACKGROUND: In clinical stage IA lung adenocarcinoma (LUAD), rapid and accurate intraoperative diagnosis is crucial to decide whether to perform segmentectomy and lobectomy. Frozen section analysis is time consuming and not always reliable for LUAD diagnosis and grading. We developed deep learning models using surgical resection images to assist in prompt diagnosis and risk stratification of stage...

Real-time prediction of emergency department admissions using an AI model and its integration into hospital bed-planning.

OBJECTIVE: To design, validate, and implement a tool based on a machine-learning model capable of predicting emergency patient admissions in real time, and to develop an application that integrates the model together with traditional indicators of health care pressure. METHODS: We analyzed anonymized data from a total of 328,696 emergency episodes. Fifteen variables with the highest predictive cap...

Machine learning to predict hospital admission at triage in paediatric emergency care: A meta-analysis.

UNLABELLED: Machine learning (ML) models have shown promise improving outcome prediction and early risk stratification in paediatric emergency departm...

[Risk adjustment as a prerequisite for quality assessment in certification and registry analyses].

BACKGROUND: Quality assessment in arthroplasty is gaining increasing importance in the context of growing transparency requirements in healthcare syst...

Combined use of serum KL-6 and respiratory functional parameters for identifying fibrotic lung damage in SARS-CoV-2-induced interstitial pneumonia.

BACKGROUND: Post-COVID19 pulmonary fibrosis (PCPF) has been reported in a significant proportion of patients who survive the acute SARS-CoV-2 infectio...

Measuring the Impact of AI on Report-Drafting Efficiency in Chest Computed Tomography Interpretation: Retrospective Analysis.

BACKGROUND: Artificial intelligence (AI), particularly deep learning, has shown promise in enhancing medical image interpretation and improving radiol...

Machine learning-based prediction of out-of-hospital births in prehospital emergency care.

BACKGROUND: Unplanned out-of-hospital births constitute rare but high-risk obstetric emergencies managed by emergency medical services (EMS). Rapid as...

Optimising pandemic response through vaccination strategies using neural networks.

Epidemic risk assessment poses inherent challenges, with traditional approaches often failing to balance health outcomes and economic constraints. Thi...

Integrated Implementation Strategies to Promote the Use of AI-Assisted Diagnostic Software for Lung Nodule Screening in China: Process Evaluation Based on the RE-AIM Framework.

BACKGROUND: While artificial intelligence (AI)-assisted diagnostic software holds promise for improving diagnostic efficiency and reducing disparities...

The role of digital tools and artificial intelligence in supporting antimicrobial stewardship: Study protocol for a systematic review.

INTRODUCTION: Since the post-antibiotic era, there has been significant difficulty in treating infectious diseases due to the increase in antimicrobia...

Ontology-Based Medication Named Entity Recognition Using Pretrained Transformer Models From a Thai Hospital: Model Fine-Tuning and Validation Study.

BACKGROUND: Extracting accurate medication information from Thai hospital records presents challenges due to the narrative style of medical notes, whi...

Effect of a clinical decision support system on stroke care quality and outcomes in patients with acute ischaemic stroke (GOLDEN BRIDGE II): cluster randomised clinical trial.

OBJECTIVE: To evaluate the efficacy of a clinical decision support system (CDSS) on stroke care quality and clinical outcomes among patients with acut...

Explainable Artificial Intelligence for Predicting Length of Stay and Treatment Response in Pediatric Asthma and Allergic Rhinitis: An Irregular Fuzzy Cellular Automata Approach Across Pre- and During-COVID-19 Periods.

OBJECTIVE: The COVID-19 pandemic significantly altered treatment strategies and healthcare utilization patterns in pediatric respiratory diseases. Giv...

Feasibility of short-term hospital mask demand forecasting using a backpropagation neural network under data scarcity.

Early-stage infectious disease outbreaks impose acute pressure on hospital supply systems, particularly for essential protective materials such as med...

Utilization and Feasibility of a Wearable Device in Patients With Sedative Effects of Drugs: Protocol for a Prospective Observational Study for the Advanced Respiratory Monitoring Events in Drug Toxicity (ARM-ED) Study.

BACKGROUND: Drug-related deaths worldwide are most commonly attributed to opioids. Opioids and other sedative drugs can cause respiratory depression a...

Effectiveness of Al-Assisted Patient Health Education Using Voice Cloning and ChatGPT: Prospective Randomized Controlled Trial.

BACKGROUND: Traditional patient education often lacks personalization and engagement, potentially limiting knowledge acquisition and treatment adheren...

Integrating machine learning and artificial intelligence in the management of Acinetobacter infections: a narrative review.

Acinetobacter baumannii, particularly in its multidrug-resistant (MDR) and carbapenem-resistant (CRAB) forms, has become a major global health concern...

Development and validation of an artificial intelligence-based model for predicting teicoplanin plasma concentrations in intensive care unit patients with pulmonary infections: a retrospective study.

INTRODUCTION: Teicoplanin is widely used to treat pulmonary infections, particularly in critically ill patients with gram-positive bacterial infection...

Practical adaptability of a pre-hospital prognostic prediction model for patients following out-of-hospital cardiac arrest during the COVID-19 pandemic.

OBJECTIVES: The overwhelmed situation under the COVID-19 pandemic has worsened the quality of emergency medical care and the mortality rate due to out...

Predictors of weight gain in under-five children with severe acute malnutrition: A machine learning analysis of a tertiary care hospital dataset.

BACKGROUND & AIMS: Nutritionally rehabilitating children admitted to hospitals with severe acute malnutrition (SAM) and acute illness can pose signifi...