Hospital-Based Medicine

[Artificial intelligence in cardiology: the new HAL 9000?].

Artificial intelligence (AI) is rapidly transforming the world, and medicine is at the forefront of this revolution. In cardiology, AI is increasingly providing innovative tools for diagnosis, risk stratification, interventional planning, and personalized care. From automated interpretation of ECGs and cardiovascular imaging to integration into interventional workflows and predictive models, AI is...

Simulation-free spine palliative radiotherapy enabled by AI-adapted diagnostic CT.

BACKGROUND: Radiotherapy planning traditionally requires a dedicated simulation CT (sCT), which can introduce delays in initiating treatment. This is particularly impactful in spinal palliative care, where timely treatment is often important for symptom control and prevention of neurological deterioration. Although diagnostic CT (dCT) is frequently available earlier in the workflow, it can lead to...

Tailored AI ethics: Enacting geriatric care with AI-based patient monitoring.

Artificial intelligence (AI) systems are increasingly integrated into clinical practice, where they demonstrate potential to mitigate adverse events t...

A fine-grained transformer combined with multimodal data for predicting hospital length of stay in acute coronary syndrome.

The prediction of hospital length of stay (LOS) is of great significance for hospitals to rationally allocate medical resources and provide timely tre...

Risk stratification for long-term inpatient costs in mental disorders: a dual-track machine learning approach using baseline EHRs and hospitalization trajectories.

Exploring the optimal age for total knee arthroplasty to minimize risk of adverse outcomes: machine learning analysis of a statewide cohort.

BACKGROUND: Rates of total knee arthroplasty (TKA) in the United States have risen in patients of a wide age range. Although rates of postoperative TK...

Evaluating Guideline-Adherent Antibiotic Use for Skin Infections Using Natural Language Processing: A Pilot Study.

OBJECTIVE: To develop and evaluate an internally validated natural language processing (NLP) model to determine guideline adherence of antibiotic deci...

Polysaccharide-functionalized gold, silver, and iron oxide nanoparticles for siRNA delivery: The role of artificial intelligence in design and optimization.

Polysaccharide-functionalized metallic nanoparticles (MFNPs), including gold (Au), silver (Ag), and iron oxide (Fe3O4), have emerged as promising nano...

SAFEGUARD: Transforming Military Suicide Prevention Through Predictive Analytics and Targeted Interventions.

PURPOSE OF REVIEW: Machine learning predictive modeling can support scalable prevention of suicide-related behavior (SRB). SAFEGUARD is a three-pronge...

Predicting mortality and short-term outcomes of continuous kidney replacement therapies in neonates and infants.

BACKGROUND: Continuous kidney replacement therapy (CKRT) has emerged as a valuable treatment option in critically ill neonates and infants with acute ...

NLP in Support of Pharmacovigilance: QUality Adverse Drug Reaction AcTIve Control (QUADRATIC).

Adverse drug reactions (ADRs) are a major cause of morbidity, hospital admissions, and in-hospital mortality, yet remain incompletely captured by post...

Re-imagining discharge summary training through artificial intelligence.

What is the educational challenge? Discharge summary (DS) writing is a core competency for junior physicians, yet persistent deficiencies in the quali...

Explainable Machine Learning for Coma Outcome Prediction Based on Structural and Functional Brain MRI.

OBJECTIVES: Advanced MRI is recommended for the clinical evaluation of patients with coma. However, the implementation of these guidelines has been hi...

When AI joins the table: evaluating large language model performance in soft tissue sarcoma tumor board decisions.

OBJECTIVES: Multidisciplinary tumor boards (MDTs) are critical for the personalized management of soft tissue sarcomas (STS), but they are limited by ...

Machine learning framework for early identification of high-spending Crohn's disease patients using administrative claims.

OBJECTIVES: To develop and evaluate a machine learning (ML) model that predicts Crohn's disease (CD) patients responsible for the top quartile of heal...

Predicting wire electrical discharge machined surface roughness of C355/silicon nitride/graphene hybrid nanocomposites using simulation, statistical and machine learning techniques.

This study employed machine learning (ML) and optimization approaches, with support vector regression (SVR), artificial neural networks (ANNs) simulat...

Distinguishing post-COVID from long-COVID in adults: Development and validation of a biomarker signature using targeted proteomics and machine learning in a cross-sectional observational study.

BACKGROUND: COVID-19 can have diverse clinical manifestations, ranging from asymptomatic infection to critical illness with multiorgan involvement. Wh...

Effectiveness of natural language intelligence technology in chronic diseases nursing: A systematic review and meta-analysis.

BACKGROUND: Chronic diseases pose a heavy global burden, with challenges in utilizing unstructured data for continuous care. Natural language intellig...

Predicting Nocturnal Hypertension in CKD Through Table-Value Diffusion Model.

BACKGROUND: Ambulatory blood pressure monitoring is indispensable for diagnosing nocturnal hypertension (NH) among patients with chronic kidney diseas...

Protocol for a pragmatic trial comparing navigator and automated virtual interventions for guaranteeing adherence to fecal immunochemical testing (NAVIGATE-FIT).

BACKGROUND: Fecal immunochemical testing (FIT) - a non-invasive colorectal cancer (CRC) screening method offers an opportunity to bridge CRC screening...