Hospital-Based Medicine

Latest AI and machine learning research in hospital-based medicine for healthcare professionals.

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Evaluating an LLM-Assisted Workflow for Clinical Documentation: A Pilot Randomized Controlled Trial on Time and Quality

Large language models (LLMs) have been investigated for clinical documentation, with concerns about ...

Machine Learning Risk Prediction for Prolonged Hospitalization in Frail Older Adults with Multimorbidity

Frailty and multimorbidity are common in older adults and contribute substantially to prolonged hosp...

Clinical Implementation of an AI Algorithm for Substance Misuse Screening in Hospitalized Adults

Manual inpatient screening for substance misuse is labor-intensive and inconsistently applied. Evalu...

Characterizing Dementia Phenotypes from Unstructured EHR Notes with Generative AI and Interpretable Machine Learning

Dementia encompasses diverse clinical syndromes where diseases of the brain can manifest as impaired...

Circulating microglia-derived extracellular vesicles predict recovery after rehabilitation in stroke survivors

Timely intensive rehabilitation is crucial to contrast the negative escalation of events that follow...

Development and validation of Retrieval Augmented Generation (RAG) and GraphRAG for complex clinical cases

Chronic Kidney Disease (CKD) is a progressive condition requiring evidence-based management, but adh...

Multidisciplinary large language model agent teams for precision oncology enhance complex gynecologic oncology decision support

Large language models can help with clinical decision-making tasks. Complex oncology cases are best ...

Analyzing Information Disparities across Modalities in Mortality Prediction

Recent advances in deep learning have enabled the integration of heterogeneous data modalities for c...

MAX-EVAL-11: A Comprehensive Benchmark for Evaluating Large Language Models on Full-Spectrum ICD-11 Medical Coding

MAX-EVAL-11 is constructed by converting MIMIC-III discharge summaries from ICD-9 to ICD-11 codes th...

Reliability of Artificial Intelligence-enhanced Electrocardiography

The scientific literature on artificial intelligence-enabled electrocardiography (AI-ECG) has define...

Measuring the Quality of AI-Generated Clinical Notes: A Systematic Review and Experimental Benchmark of Evaluation Methods

High-quality clinical documentation is essential for safe, effective care, yet producing it is time ...

Deceptive Bias Measurement in Deep Learning: Assessing Shortcut Reliance in TCGA Cancer Models

Bias in machine learning is a persistent challenge because it can create unfair outcomes, limit gene...

Machine Learning Analysis of Post-Acute COVID Symptoms Identifies Distinct Clusters, Severity Groups, and Trajectories

Questionnaires that capture patient-reported symptomatology provide low-cost but potentially high-va...

Clustering high-cost patients in England using machine learning: a population-based cohort study

To identify clusters of high-cost patients in England based on diagnoses and sociodemographic charac...

A Comparison of Two Deep Learning Approaches to Distinguish Functional Dissociative from Epileptic Seizures Using Event Videos

Differentiating between motor functional dissociative seizures (FDS) and motor epileptic seizures (E...

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