Hospitalists

Development of Interactive Nomograms for Predicting Short-Term Survival in ICU Patients with Aplastic Anemia

Aplastic anemia is a severe hematologic disorder marked by pancytopenia and bone marrow failure. ICU admission often reflects disease progression or complications requiring critical care. Predicting short-term survival in these patients is vital for individualized treatment and resource optimization. Nomograms provide a practical tool for integrating clinical parameters, offering accurate visualiz...

Utilizing Machine Learning Models to Predict Acute Kidney Injury in Septic Patients from MIMIC-III Database

Sepsis is a severe condition that causes the body to respond incorrectly to an infection. This reaction can subsequently cause organ failure, a major one being acute kidney injury (AKI). For septic patients, approximately 50% develop AKI, with a mortality rate above 40%. Creating models that can accurately predict AKI based on specific qualities of septic patients is crucial for early detection an...

Predicting 30-Day Mortality and Readmission Using Hospital Discharge Summaries: A Comparative Analysis of Machine Learning Models, Large Language Models, and Physicians

This study evaluates the comparative performance of trained machine learning models, commercial off-the-shelf (COTS) large language models (LLMs), and...

Towards Inpatient Discharge Summary Automation via Large Language Models: A Multidimensional Evaluation with a HIPAA-Compliant Instance of GPT-4o and Clinical Expert Assessment

Large language models (LLMs) have demonstrated potential to automate clinical documentation tasks that may reduce clinician burden, such as generation...

Integrating a host transcriptomic biomarker with a large language model for diagnosis of lower respiratory tract infection

Lower respiratory tract infections (LRTIs) are a leading cause of mortality worldwide and can be difficult to diagnose in critically ill patients, as ...

DISCO: A Delta-NIHSS-Based Machine Learning Model for Predicting Recurrence, Disability, and Mortality Following Acute Ischemic Stroke

An accurate, robust, clinically accessible, and explainable predictive model for post-stroke composite outcomes could identify high-risk patients for ...

Machine Learning-based Mortality Prediction for Pediatric Fulminant Myocarditis Using Cytokine Profiles

Fulminant myocarditis (FM) is a rare but life-threatening pediatric condition that rapidly progresses to cardiogenic shock and fatal arrhythmias. Earl...

Predicting ICU Readmission in Acute Pancreatitis Patients Using a Machine Learning-Based Model with Enhanced Clinical Interpretability

Acute pancreatitis (AP) is a common and potentially life-threatening gastrointestinal disease that places a substantial burden on healthcare systems w...

Development of the Short Hospitalization Predictor (SHoP) Machine Learning Model Across Two Hospitals

To develop and evaluate an open-source machine learning (ML) models for predicting hospital short stays (length of stay [LOS] under 48 and 72 hours) e...

Interpretable Hazard Models Reveal Strong Metastasis Dependence and Feature Interaction Effects in Predicting Cancer Patient Readmission Risk

Predicting hospital readmission in cancer patients-particularly those with metastatic disease-remains a significant clinical challenge. While metastas...

Predicting the need for electroconvulsive therapy via machine learning trained on electronic health record data

Electroconvulsive therapy (ECT) is an effective treatment of severe manifestations of mental illness. Since delay in initiation of ECT can have detrim...

Urinary collagen peptides predict mortality

Organ fibrosis caused by the presence of excessive extracellular matrix (ECM) is strongly related to mortality. Urinary peptide signatures were report...

Nucleotide motif-guided selection of plasma microRNA biomarkers for organ injury prediction in trauma

Trauma remains a leading cause of morbidity and mortality in part due to secondary organ injury and infection. Yet, our ability to predict the downstr...

Verifiable Summarization of Electronic Health Records Using Large Language Models to Support Chart Review

Information overload in electronic health records (EHRs) hampers clinicians’ ability to efficiently extract and synthesize critical information from a...

Case-Control Matching Erodes Feature Discriminability for AI-driven Sepsis Prediction in ICUs: A Retrospective Cohort Study

Sepsis remains a leading cause of intensive care unit (ICU) mortality worldwide, and early detection is essential for improving survival through timel...

Zero-Shot Large Language Models for Long Clinical Text Summarization with Temporal Reasoning

Recent advances in large language models (LLMs) have shown potential in clinical text summarization, but their ability to handle long patient trajecto...

clickBrick Prompt Engineering: Optimizing Large Language Model Performance in Clinical Psychiatry

Prompt engineering has the potential to enhance large language models’ (LLM) ability to solve tasks through improved in-context learning. In clinical ...

AI-generated patient-friendly discharge summaries to empower patients

Patients often struggle to fully understand their discharge letters after inpatient hospital stays, which are often replete with domain-specific medic...

Large Language Models Improve Coding Accuracy and Reimbursement in a Neonatal Intensive Care Unit

Diagnosis coding is essential for clinical care, research validity, and hospital reimbursement. In neonatal settings, manual coding is frequently erro...

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...