Infection Control

Artificial intelligence in trauma care: applications, ethical challenges, and pathways toward responsible integration.

PURPOSE OF REVIEW: Artificial intelligence is increasingly applied across the trauma care continuum, from prehospital triage to in-hospital decision-making. This review provides a timely synthesis of emerging applications, ethical challenges, and regulatory frameworks shaping the responsible integration of artificial intelligence into trauma systems. RECENT FINDINGS: Recent studies highlight the p...

From clinics to the environment: A systematic review of machine learning and MALDI-TOF MS in the identification of antimicrobial-resistant bacteria.

As a result of the increasing prevalence of Antibiotic-resistant bacteria (ARB) and antibiotic-resistant genes (ARGs) in both community and hospital settings, their identification by conventional approaches has been posing a significant issue for decades. A new approach, integrating matrix-assisted laser desorption/ionization time of-flight mass spectrometry (MALDI-TOF-MS) with machine learning (M...

Use of Machine Learning to Predict Hospital Admission for EMS-Treated Infants After a Suspected BRUE.

OBJECTIVES: This study explored the use of different applied machine learning (ML) classification algorithms to predict hospital admission for infants...

Development and internal validation of machine learning in predicting prognosis of acute kidney injury patients in resource-limited setting.

BACKGROUND: Machine learning models for predicting acute kidney injury (AKI) prognosis have primarily been developed in resource-rich settings, with l...

Predicting recurrent cardiac arrest within one year after surviving In-Hospital Cardiac Arrest using a machine learning model.

BACKGROUND: Patients discharged alive after in-hospital cardiac arrest (IHCA) have an increased mortality up to a year after hospital discharge. Impro...

Learner-Centered Microbiology Teaching via the Flipped Classroom Model: Insights from a Mixed-Methods Educational Study.

CONTEXT: Medical education has evolved to emphasize active learning and technology for competency development. The flipped classroom (FCR) model shift...

Clinical-Radiomics hybrid prediction of the Risks of pedicle screw loosening after posterior lumbar fusion.

OBJECTIVE: We aimed to construct a risk prediction model for PSL after posterior lumbar fusion using machine learning and radiomic methods. SUMMARY OF...

A randomized controlled trial of artificial intelligence-based analytics for clinical deterioration.

This pragmatic randomized controlled trial aimed to assess the effect of a passive display of artificial intelligence (AI)-based predictive analytics ...

Clinical Profile of Neurosurgical Consultations from the Emergency Department : A Retrospective Analysis.

OBJECTIVE: This study aimed to retrospectively analyze consultations requested from the emergency departments (EDs) to the neurosurgery (NS) departmen...

Forecasting Local Surges in COVID-19 Hospitalizations through Adaptive Decision Tree Classifiers.

IntroductionDuring the COVID-19 pandemic, many communities across the United States experienced surges in hospitalizations, which strained the local h...

Machine learning for optimizing mAs in KUB radiography with metal implants.

BACKGROUND AND PURPOSE: Kidney-ureter-bladder (KUB) radiography is a common examination that exposes patients to a higher radiation dose and increased...

Early prediction of colonization by carbapenemase-producing enterobacterales at ICU admission using machine learning.

Colonization by carbapenemase-producing Enterobacterales (CPE) on admission to an intensive care unit (ICU) poses a serious threat to infection contro...

Detection of Antithrombotic-Related Bleeding in Older Inpatients: Multicenter Retrospective Study Using Structured and Unstructured Electronic Health Record Data.

BACKGROUND: Bleeding complications are a major contributor to adverse drug events among older inpatients, particularly in those treated with antithrom...

A comparative evaluation of time-series models for forecasting inpatient deaths and discharges against medical advice.

Forecasting inpatient mortality (IM) and discharges against medical advice (DAMA) provides essential insights for healthcare quality monitoring and ho...

Machine learning for identifying risk factors of nosocomial infection in cancer patients with immune checkpoint inhibitor-related pneumonia.

Early prediction of pressure injury risk in hospitalized patients using supervised machine learning models based on nursing records.

This study aimed to employ supervised models for predicting pressure injuries in hospitalized patients using data collected within the first eight hou...

PreLora: A fine-tuning approach with low-rank matrix decomposition and prefix tuning for pre-hospital emergency text classification.

OBJECTIVE: With expanding applications of artificial intelligence technology in the medical field, Large Language Models (LLMs) have achieved substant...

Prehospital transfusion strategies in haemorrhagic shock.

BACKGROUND: Massive transfusion protocols are established in-hospital practices for managing haemorrhagic shock, yet critical bleeding accounts for up...

Assessing in-hospital mortality risk in ICU lung cancer patients using machine learning: An analysis based on the MIMIC-IV database.

BACKGROUND: Patients with advanced lung cancer admitted to the intensive care unit (ICU) face a substantially elevated risk of in-hospital mortality. ...

An artificial intelligence-powered learning health system to improve sepsis detection and quality of care: a before-and-after study.

Sepsis is a major global health crisis where early recognition and effective management remain significant challenges for healthcare systems. As part ...