Hospitalists

Antimicrobial resistance analysis of Klebsiella pneumoniae bloodstream infections based on a random forest algorithm: a longitudinal study based on data from tertiary hospitals in China from 2012 to 2023.

BACKGROUND: Bloodstream infections (BSIs) caused by Klebsiella pneumoniae pose a significant global health burden, complicated by rising antimicrobial resistance (AMR). This study aimed to characterize resistance patterns, identify predictors of carbapenem resistance, and develop a machine learning model to predict patient outcomes. METHODS: In a retrospective analysis of 109 279 K. pneumoniae BSI...

Development and Clinical Utility of Machine Learning Models for Prediction of Same-Day Discharge in Outpatient Hip and Knee Replacement: A Prognostic Study.

BACKGROUND: Hip and knee replacement are common procedures with an increasing focus on same-day surgery. However, capacity constraints limit the number of eligible patients actually being scheduled for same-day discharge, calling for further selection of those with the highest likelihood of same-day discharge. METHODS: A prognostic study from September 2022 to April 2024 aiming to develop and eval...

An informatics framework to harmonize electronic health record medication data for managed care analytics and artificial intelligence applications.

BACKGROUND: Artificial intelligence (AI) applications in managed care pharmacy depend on semantically consistent medication data, yet heterogeneous me...

Evaluating the Accuracy, Empathy, and Readability of Generative AI Versus Registered Nurses in Discharge Planning: A Vignette-Based Study.

AIM: To compare the multidimensional performance of discharge instructions generated by generative AI (GPT-4) versus those created by clinical registe...

The Digital Transformation of Rehabilitation Medicine: A Narrative Review of Artificial Intelligence Innovations, Clinical Integration, and Future Paradigms.

BACKGROUND: Traditional rehabilitation medicine, primarily dependent on qualitative clinical assessment and static therapeutic protocols, faces signif...

Effect of a Machine Learning Algorithm to Guide Goal-Directed Therapy After Cardiac Surgery.

BACKGROUND: Goal-directed therapy allows clinicians to optimize perfusion and volume status in patients postoperatively. OBJECTIVE: To evaluate the ef...

Machine-based classification of epileptiform activity in selected EEG excerpts from genetic generalised epilepsy.

OBJECTIVES: To evaluate whether the deep learning model IGENet-TS, a time-domain convolutional neural network (CNN), can classify expert-selected EEG ...

Machine learning-based prediction of fall within 6 months of stroke onset: A prospective study.

OBJECTIVE: To develop and evaluate machine learning-based models for predicting fall risk within 6 months of stroke onset. METHODS: This prospective s...

Machine learning prediction of 30-day mortality in coronary artery disease: a retrospective multicenter study using electronic health records.

BACKGROUND: Coronary artery disease (CAD) is the leading cause of death globally and a major contributor to hospital readmission. This study aimed to ...

Stress hyperglycaemia ratio and post-operative delirium after cardiac surgery: a retrospective cohort study of the MIMIC-IV database.

BACKGROUND: Post-operative delirium is a serious neurocognitive complication of cardiac surgery. The stress hyperglycaemia ratio (SHR), which adjusts ...

Uncovering hidden industrial wastewater discharges in urban rivers via deep learning-enhanced fiber-optic distributed acoustic sensing.

Illicit wastewater discharges from concealed outfalls threaten urban river ecosystems, often evading conventional monitoring. This study introduces an...

AI-assisted radiographic fracture detection and length of stay in the adult ambulatory orthopedic emergency department: a before-after cohort study with a disease-specific internal control.

BACKGROUND: Although artificial intelligence-assisted radiographic fracture detection tools (AI-RFDT) have demonstrated high diagnostic accuracy in ad...

A grade-specific clinical prediction rule for independent walking after traumatic spinal cord injury.

OBJECTIVE: To identify significant predictors for individual American Spinal Injury Association Impairment Scale (AIS) grades, and develop a clinical ...

Machine Learning Predicts Functional Decline and Risk Phenotypes in Older Patients With Heart Failure.

BACKGROUND: Preserving functional independence is critical in older patients with heart failure (HF), yet tools predicting long-term functional trajec...

Global estimation of bankfull river discharge reveals distinct flood recurrences across climate zones.

Bankfull discharge, the maximum flow a river can convey before spilling over its banks, is central to modelling flood risk and understanding river-cha...

Vibe Coding for Statistical Analysis Using Large Language Models.

BACKGROUND: Large language models have accelerated the adoption of generative artificial intelligence (AI), making AI tools more widely accessible thr...

Value of AI in Critical Care Using Real-World Evidence on Intensive Care Unit Mortality Prediction: Cost-Utility Analysis.

BACKGROUND: AI-based models for predicting mortality have shown potential for intensive care unit (ICU) patients, but evidence regarding their cost-ef...

A machine learning approach to predict river hydrographs for water quality sampling.

Hydrology and water quality are innately linked, as flow dynamics control the transport of pollutants within river systems. Consequently, the timing o...

AI-generated patient instructions as safety-critical communication: a provisional evidence-informed framework for clinical deployment.

Generative artificial intelligence (AI) can convert clinical information into patient-facing instructions, including discharge summaries, medication e...

The role of artificial intelligence in predicting readmission risk in emergency patients: An umbrella review.

BACKGROUND: Hospital readmission following emergency care remains a persistent challenge, reflecting gaps in care continuity, discharge planning, and ...