Hospital-Based Medicine

Optimizing Oncology Care Through Collaborative Teams.

The global burden of cancer is significant, with millions of new diagnoses expected annually, necessitating highly coordinated and patient-centered care. To address workforce shortages, increasing case complexity, and high rates of clinician burnout, ASCO advocates for a transition from traditional physician-centric models to interdependent, multidisciplinary team-based care. This paper outlines t...

"Early Stratification of Risk for Poor Neurological Outcome After Cardiac Arrest Is Improved with Processed EEG Data".

AIM: To evaluate the impact of adding early processed quantitative EEG biomarkers to health record data for early neurological risk stratification after cardiac arrest using machine learning. METHODS: Data available during ICU admission after return of spontaneous circulation (ROSC) were collected from comatose patients, including the processed EEG metrics suppression ratio (SR) and bispectral ind...

An artificial intelligence-based endoscopic ultrasonography risk assessment and stratification for gastric stromal tumors.

BACKGROUND: Gastrointestinal stromal tumors (GISTs) are tumors with malignant potential. This research aims to develop an artificial intelligence (AI)...

Machine learning models for predicting neonatal bacterial infections: a retrospective cohort study.

Bacterial infections represent a critical threat to neonatal health, accounting for approximately 25% of neonatal mortality globally. Timely and preci...

Exploratory multivariate adaptive regression splines modeling of myopic progression in children receiving atropine.

BACKGROUND: The increasing global prevalence of pediatric myopia has led to the widespread use of atropine for myopia control. Despite its proven effi...

When algorithms don't care: physiotherapy and the post-professional turn.

Physiotherapy has long resisted the forces that restructured manufacturing, journalism, and finance. This resistance is ending. Drawing on Gilles Dele...

Construction of an associative model for prolonged intensive care unit stay in sepsis patients combined with myocardial injury.

OBJECTIVE: This study aimed to identify the factors contributing to Prolonged Length of Stay (PLOS) in intensive care units for sepsis patients combin...

A large language model-driven multidisciplinary AI agent system predicts delirium in emergency critically ill patients.

Delirium occurs frequently in emergency departments and is associated with poor outcomes and increased burden. Early delirium risk prediction is cruci...

Artificial Intelligence-Enabled Electrocardiography for Monitoring Serum Potassium Dynamics in Patients With Severe Hypokalemia.

RATIONALE & OBJECTIVE: Severe hypokalemia requires prompt management and close surveillance. Although artificial intelligence-enabled electrocardiogra...

Histopathological Differences Between Idiopathic and Secondary Interstitial Pneumonia: An Elastic Fiber-Oriented Deep Learning Analysis.

Fibrotic interstitial lung diseases (ILDs) are classified into histopathological patterns by the idiopathic interstitial pneumonia (IIP) classificatio...

Identification of weight loss predictors using machine learning approaches in adolescents with obesity.

BACKGROUND: Despite the effectiveness of lifestyle multidisciplinary (LMD) weight loss interventions in pediatric obesity, outcomes remain variable be...

The CODEX action incubator: a consensus-driven approach to identify and implement diagnostic excellence measures in the context of artificial intelligence.

Diagnostic errors are a substantial source of patient harm. As artificial intelligence (AI) integrates into clinical workflows, opportunities are emer...

Enhancing diagnostic safety: addressing knowledge gaps for using human factors tools in the safe and effective use of AI - a proposed research agenda.

INTRODUCTION: Identify knowledge gaps in applying artificial intelligence in clinical settings, using medical imaging as a primary use case to enhance...

Algorithmic fairness in machine-learning models for determining the progression or recurrence of cardiovascular disease in racialized populations: a scoping review.

OBJECTIVE: To examine how algorithmic fairness is measured, operationalized, and reported in machine learning (ML) models designed to predict or suppo...

Development and Validation of a Machine Learning-Based Bedside Score (IPA-2T2C2DW) for Invasive Pulmonary Aspergillosis in Critically Ill Non-Neutropenic Immunocompromised Hosts.

OBJECTIVE: To develop and validate machine learning-based diagnostic models for IPA using data available within 24 hours of ICU admission, construct t...

Postoperative Nocardia cyriacigeorgica infection after glioblastoma resection: correlating metagenomic next-generation sequencing with conventional microbiology.

Postoperative nocardial infection after cranial surgery is rare and difficult to diagnose because Nocardia spp. grow slowly in conventional culture. M...

Clinically interpretable prediction models of stroke functional outcomes: A national cohort study of adults in inpatient rehabilitation facilities in the US.

OBJECTIVE: To predict self-care and mobility function at discharge from inpatient rehabilitation for adults with stroke using only variables from the ...

AI in GME education.

Artificial intelligence (AI) is rapidly being integrated across the landscape of graduate medical education (GME) with applications spanning the resid...

ASO Visual Abstract: Concordance Between a Generative Artificial Intelligence Model and a Hepatobiliary Multidisciplinary Team in Hepatocellular Carcinoma Management: A Retrospective Study.

[Advances in phage therapy for pneumonia caused by Klebsiella pneumoniae].

Klebsiella pneumoniae (KP) has emerged as a formidable nosocomial pathogen in the era of antimicrobial resistance, with mortality from pneumonia cause...