Infection Control

An integrated random forest- and LASSO-derived nomogram for predicting postoperative nosocomial infections in colorectal cancer patients.

Interpretable machine learning for chronic kidney disease prediction: Insights from SHAP and LIME analyses.

Chronic kidney disease (CKD) is a progressive condition requiring early detection for optimal patient outcomes. This study developed an interpretable machine learning framework using XGBoost with SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) for transparent CKD prediction. We evaluated the approach on two datasets: UAE Tawam Hospital data (n = 491)...

Comprehensive assessment of postoperative metachronous liver metastasis risk in colon cancer based on inflammatory indicators: a multicenter prospective study.

OBJECTIVE: Insidiousness is a hallmark of metachronous liver metastasis. Owing to the absence of a comprehensive machine-learning model integrating sy...

AI-ECG classification for Brugada syndrome: A study of machine learning techniques to optimise for limited datasets.

Deep neural networks can classify ECGs with high accuracy when training data is abundant. Rare conditions like Brugada syndrome, an inherited arrhythm...

Non-pharmacological treatment of AECOPD.

Acute exacerbations of chronic obstructive pulmonary disease (AECOPDs) are acute events characterized by rapid worsening of dyspnea, cough, and sputum...

The prognostic value of the PALBI score for mortality prediction in stroke: an internal and external validation study.

Recent studies show that there's a link between liver problems and how well someone does after having a stroke. The platelet-albumin-bilirubin (palbi)...

Protocol for the development and validation of a questionnaire to evaluate user-robot interaction in the compounding of hazardous drugs in a hospital setting.

INTRODUCTION: The automation of hazardous drug preparation in hospitals using robotic systems is an effective strategy to enhance safety, quality, and...

End-to-end integrative segmentation and radiomics prognostic models for risk stratification of high-grade serous ovarian cancer: a retrospective multicohort study.

BACKGROUND: Valid stratification factors for patients with epithelial ovarian cancer are still lacking and individualisation of care remains an unmet ...

Label-Free Differentiation of Antimicrobial Resistance Groups Using Raman Spectroscopy.

Increasing antimicrobial resistance (AMR) has developed into an enormous health burden. Here, a systematic investigation was conducted to evaluate the...

Predicting Geriatric Rehabilitation Stays of ≤4 Weeks After Hip Fracture Surgery: Machine Learning Approach Using Physical Activity and Patient Data.

BACKGROUND: In 2022, over 18,000 patients aged ≥70 years were hospitalized in the Netherlands for a hip fracture, with 50% requiring geriatric rehabil...

AI-Based STroke Risk fActor Classification and Treatment (ABSTRACT) study.

BACKGROUND: Stroke is a leading cause of death and disability worldwide, costing the UK approximately £26 billion annually. While lifestyle modificati...

The Evaluation of Transformer Models for the Detection of Adverse Drug Events: A Benchmark Study Using Dutch Free-Text Documents of Hospitalized Patients.

INTRODUCTION: Adverse drug events (ADEs) are a leading cause of preventable patient harm in hospitals. Because they are often recorded only in clinica...

Predicting surgical intensive care unit readmission with machine learning model: Bi-center training and validation.

BACKGROUND: Patients readmitted to the surgical intensive care unit (SICU) face a high risk of mortality and increased hospital costs. Identifying pat...

A deep learning model integrating structured data and clinical text for predicting atrial fibrillation recurrence.

Multimodal perioperative data from patients undergoing atrial fibrillation (AF) ablation offer valuable insights for stratifying recurrence risk, yet ...

Personalized sepsis mortality prediction: An interpretable machine learning nomogram.

PURPOSE: Sepsis remains a major cause of mortality in ICU patients, requiring accurate prognostic tools for optimal management. This study aimed to de...

Development and external validation of a machine learning model for predicting in-hospital mortality in acute liver failure.

BACKGROUND: Acute liver failure (ALF) is a rapidly progressive and life-threatening condition that requires accurate risk stratification. Existing pro...

Predicting the need for electroconvulsive therapy via machine learning trained on electronic health record data.

OBJECTIVES: Electroconvulsive therapy (ECT) is an effective treatment of severe manifestations of mental illness. Since delay in initiation of ECT can...

Machine learning-based on model for explain risk of 24-hour death in critically ill patients in the prehospital setting: A retrospective cohort study.

This study aimed to develop and validate a machine learning-based model for predicting 24-hour mortality in critically ill patients using prehospital ...

A Large Language Model Approach to Functional Status Scale Assessment.

OBJECTIVES: To develop a fine-tuned version of the generative pretrained transformer (GPT)-4o artificial intelligence (AI) model able to estimate Func...

Data-driven subgroups for 3-year risk stratification of incident diabetes and complications in diabetes-free Chinese adults.

BACKGROUND: Substantial metabolic heterogeneity exists prior to the development of diabetes, creating opportunities for earlier and more precise inter...