Infection Control

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 treated by emergency medical services (EMS) after a suspected brief resolved unexplained event (BRUE). METHODS: Data from a large regionalized pediatric care system were obtained for infants in which paramedic suspected a BRUE and who were transport...

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 limited validation in resource-limited environments. This study applied machine learning techniques to predict in-hospital mortality and major adverse kidney events within 28 days (MAKE-28) among critically ill patients with AKI in Southeast Asia. MET...

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 ...

AI-Assisted detection of corneal nerve structural abnormalities in early diabetic keratopathy: development and validation of a deep learning framework.

To develop a deep learning-based computer-aided diagnostic model for the automated identification of corneal microneuromas from in vivo confocal micro...

Interpretable machine learning model for predicting in-hospital mortality in elderly acute pancreatitis: Development and validation in a multicenter cohort.

BACKGROUND: Elderly acute pancreatitis (AP) patients face significantly higher in-hospital all-cause mortality, highlighting the need for effective ri...