Hospital-Based Medicine

Automated Evaluation of Large Language Model Response Concordance with Human Specialist Responses on Physician-to-Physician eConsult Cases

Specialist consults in primary care and inpatient settings typically address complex clinical questions beyond standard guidelines. eConsults have been developed as a way for specialist physicians to review cases asynchronously and provide clinical answers without a formal patient encounter. Meanwhile, large language models (LLMs) have approached human-level performance on structured clinical task...

Sex-Specific Diagnostic Subtypes in Adolescents Hospitalized for Substance Use Disorders Revealed by Transformer-Based Clustering

Substance use disorders (SUD) are a leading cause of psychiatric hospitalization among adolescents, yet the underlying diagnostic profiles and comorbidities remain poorly characterized. Here, we applied a transformer-based language model to 4,849 hospital discharge records from adolescents (aged 11–18) admitted with mental health and SUD in Spain between 2016 and 2020. We generated dense clinical ...

Toward Digital Twins in the Intensive Care Unit: A Medication Management Case Study

To evaluate the efficacy of digital twins developed using a large language model (LLaMA-3), fine-tuned with Low-Rank Adapters (LoRA) on ICU physician ...

Analysis of Genome-Wide Cell-Free DNA Fragment Length Distributions in Colorectal Cancer

Each piece of cell-free DNA (cfDNA) has a length determined by the exact metabolic conditions in the cell it belonged to at the time of cell death. Th...

Characterizing and Predicting End-of-Life Patient Trajectories Using Routine Clinical Data

Understanding the biological processes that precede death is critical for making informed clinical decisions and facilitating care transitions. Here, ...

Assessing the Quality of a Personalized Prompt Generator and AI-Chatbot (ChatGPT) for Dietary and Exercise Planning in Obese Adults Using the Fuzzy Delphi Method

The potential of artificial intelligence (AI) to personalize dietary and exercise advice for obesity management is increasingly evident. However, the ...

Development and validation of electronic health record-based ascertainment of obsessive-compulsive disorder cases and controls

Obsessive-compulsive disorder (OCD) is a common psychiatric disorder, with two-thirds of affected individuals reporting severe impairment. Despite its...

Diagnostic Codes in AI prediction models and Label Leakage of Same-admission Clinical Outcomes

Artificial intelligence (AI) and statistical models designed to predict same-admission outcomes for hospitalized patients, such inpatient mortality, o...

Assessment of Medication Adherence in Patients: Development and Validation of a Machine Learning Model

This study addresses limitations of traditional medication adherence assessment tools by developing a machine learning model to evaluate post-discharg...

Predicting Intentional Self-Harm Following Psychiatric Discharge in Catalonia, Spain: Machine Learning Models from Linked Registry Data

Patients recently discharged from psychiatric hospitalization are at increased risk of intentional self-harm, including suicide. Using linked populati...

AI-based synthetic simulation CT generation from diagnostic CT for simulation-free workflow of spinal palliative radiotherapy

Current radiotherapy (RT) planning workflows rely on pre-treatment simulation CT (sCT), which can significantly delay treatment initiation, particular...

CardiacGPT™: A Real-Time AI Assistant for Intraoperative Guidance and Postoperative Decision Support in Cardiac Surgery

Cardiac surgery is one of the most complex and high-stakes areas of medicine, where intraoperative decisions must be made within seconds and incomplet...

Clinical evaluation of a natural language processing system for assisting structured diagnosis recording at the point of care: MiADE (Medical Information AI Data Extractor)

Structured recording of key information such as diagnoses is essential for safe, efficient patient care, but is currently done incompletely because it...

Predicting Hospital Admissions Using Pretrained EHR Embeddings: External Evaluation and Insights on Local Vocabulary Adaptation

Unplanned hospital admissions impose substantial strain on healthcare systems, yet predictive models for these events remain underexplored in practice...

Harnessing Machine Learning for Antimicrobial Resistance Surveillance in Zimbabwe

Antimicrobial resistance (AMR) poses a significant public health challenge, particularly in resource-limited settings such as Zimbabwe, where surveill...

Correcting Algorithmic Bias in Machine Learning Prediction of Healthcare utilization in India

This study investigates how historical disparities in healthcare access influence machine learning (ML) predictions of healthcare utilization among ol...

Machine learning models for early prognosis prediction in cardiogenic shock

Cardiogenic shock (CS) is a severe and frequent complication of acute myocardial infarction (AMI), necessitating rapid and accurate prognosis as-sessm...

Improving the Prediction of Unplanned 30-day Cancer Readmissions Using Social Determinants of Health: A Geocoding-based Approach

Unplanned cancer readmissions present a significant burden on patients and hospitals. Current predictive models often overlook socioeconomic factors s...

A Multi-Task Deep Learning Model for Pediatric Echocardiography Analysis

Congenital heart defects afflict nearly 1% of all births worldwide. While deep learning algorithms have shown significant promise in automating and im...

Protocol for Radiographer x AI led discharge

Emergency Department (ED) overcrowding, often exacerbated by prolonged patient length of stay (LOS), is a global challenge. Patients presenting with s...