Infection Control

A comprehensive study based on machine learning models for early identification Mycoplasma pneumoniae infection in segmental/lobar pneumonia.

Segmental/lobar pneumonia in children following Mycoplasma pneumoniae (MP) infection has a significant threat to the children's health, so early recognition of MP infection is critical to reduce the severity and improve the prognosis of segmental/lobar pneumonia in children. In this study, we aim to build predictive models using machine learning techniques to assist clinicians in the early identif...

Deep learning classification of drug-related problems from pharmaceutical interventions issued by hospital clinical pharmacists during medication prescription review: a large-scale descriptive retrospective study in a French university hospital.

OBJECTIVES: Pharmaceutical interventions are proposals made by hospital clinical pharmacists to address sub-optimal uses of medications during prescription review. Pharmaceutical interventions include the identification of drug-related problems, their prevention and resolution. The objective of this study was to exploit a newly developed deep neural network classifier to identify drug-related prob...

The early prediction of neonatal necrotizing enterocolitis in high-risk newborns based on two medical center clinical databases.

: Early identification and timely preventive interventions play an essential role for improving the prognosis of newborns with necrotizing enterocolit...

A machine learning model for predicting severity-adjusted in-hospital mortality in pneumonia patients.

OBJECTIVE: This study aims to develop a customized severity adjustment tool for hospital deaths in pneumonia patients considering characteristics of K...

Rapid prediction of antibiotic resistance in complex using whole-genome and metagenomic sequencing.

Clinical management and surveillance of the complex (ECC) face significant challenges due to inaccurate species identification and prolonged turnarou...

Electronic Health (eHealth) and Artificial Intelligence-based Tools to Optimize In-hospital Patient Flow: A Scoping Review.

OBJECTIVES: Congested hospitals are increasingly common. Electronic health (eHealth) and artificial intelligence (AI)-based tools may improve in-hospi...

Leveraging Hospital Information Data for Effective Antibiotic Stewardship.

Clinical informatics has emerged as a valuable approach to enhance antimicrobial stewardship programs in healthcare settings. By integrating informati...

Performance of Machine Learning Models in Predicting 30-Day General Medicine Readmissions Compared to Traditional Approaches in Australian Hospital Setting.

: Hospital readmissions are a key quality metric impacting both patient outcomes and healthcare costs. Traditional logistic regression models, includi...

Using interpretable survival analysis to assess hospital length of stay.

Accurate in-hospital length of stay prediction is a vital quality metric for hospital leaders and health policy decision-makers. It assists with decis...

Influence of the CONCERN Early Warning System on Unanticipated ICU Transfers, In-Hospital Mortality, and Length of Stay: Results from a Multi-site Pragmatic Randomized Controlled Clinical Trial.

Communicating Narrative Concerns Entered by RNs Early Warning System (CONCERN EWS) is a machine-learning predictive model that leverages nursing surve...

Beyond episodic early warning systems: a continuous clinical alert system for early detection of in-hospital deterioration.

Efficient patient monitoring on the medical-surgical wards is crucial to prevent significant in-hospital adverse events. Standard episodic inpatient a...

A Prospective Comparative Study of the Effectiveness of Flap Fixation by Suture Versus Conventional Closure in Reducing Seroma Formation After Modified Radical Mastectomy.

Background Seroma formation is a common postoperative complication of modified radical mastectomy (MRM), leading to delayed wound healing, increased i...

Detection of ESBL-producing complex with VITEK 2 system and screening cutoffs for implementing confirmatory tests.

complex (C) are important nosocomial pathogens that can be reservoirs of transmissible extended-spectrum β-lactamase (ESBL) genes. Therefore, it is e...

Anomaly Detection in Electronic Health Records Across Hospital Networks: Integrating Machine Learning With Graph Algorithms.

In a large hospital system, a network of hospitals relies on electronic health records (EHRs) to make informed decisions regarding their patients in v...

Machine learning approach for optimizing usability of healthcare websites.

In today's digital era, hospital websites serve as crucial informational resources, providing patients with easy access to medical services. Ensuring ...

Development of explainable artificial intelligence based machine learning model for predicting 30-day hospital readmission after renal transplantation.

BACKGROUND: Hospital readmission following renal transplantation significantly impacts patient outcomes and healthcare resources. While machine learni...

Predicting mortality and risk factors of sepsis related ARDS using machine learning models.

Sepsis related acute respiratory distress syndrome (ARDS) is a common and serious disease in clinic. Accurate prediction of in-hospital mortality of p...

Utilizing machine learning and geographic analysis to improve Post-crash traffic injury management and emergency response systems.

Traffic injuries are a major public health concern globally. This study uses machine learning (ML) and geographic analysis to analyse road traffic fat...

Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial.

The COmmunicating Narrative Concerns Entered by RNs (CONCERN) early warning system (EWS) uses real-time nursing surveillance documentation patterns in...

Machine learning-driven prediction of hospital admissions using gradient boosting and GPT-2.

BACKGROUND: Accurately predicting hospital admissions from the emergency department (ED) is essential for improving patient care and resource allocati...