Hospitalists

Classification of Recurrence Status After Surgical Treatment of Chronic Subdural Hemorrhage - A Machine Learning Approach

Background Chronic subdural hematoma (cSDH) recurrence requiring reoperation occurs in 5-33% of cases, representing a substantial clinical and economic burden. The ability to predict recurrence could enable risk-stratified surveillance protocols, potentially reducing imaging burden in low-risk patients while maintaining close monitoring for high-risk individuals. We evaluated whether machine learn...

A Novel Dual-Outcome Risk Calculator for Trial of Labor After Cesarean

Objective: To develop and validate a multivariable prediction model and clinically actionable risk score for vaginal birth after cesarean (VBAC) success using machine learning, and to integrate neonatal morbidity outcomes into a decision-analytic framework for trial of labor after cesarean (TOLAC) counseling. Methods: We performed a retrospective cohort study of 1,418 consecutive TOLAC cases at a ...

Sentiment in Clinical Notes: A Predictor for Length of Stay?

Background: Length of stay (LOS) is a critical metric for hospital operational efficiency. While structured clinical data is widely used to predict LO...

Context-Aware Emergency Department Triage Using Pairwise Comparisons and Bradley-Terry Aggregation

Objective: To evaluate a ranking approach for emergency department (ED) waiting room prioritization that uses pairwise clinical comparisons aggregated...

Condition-Specific Readmission Risk Stratification in a Predominantly Black Statewide Cohort Using Machine Learning: Development of Subtype-Specific Models for Heart Failure, Acute Myocardial Infarction, Atrial Fibrillation/Flutter, and Hypertensive Heart Disease

Background: Cardiovascular disease (CVD) readmissions impose substantial clinical and economic burden. Machine learning (ML) may improve risk stratifi...

Characterizing Autonomic Dysfunction during Resuscitation in Sepsis using Multiscale Entropy

Rationale Autonomic dysfunction is a hallmark of sepsis pathophysiology, yet its quantification remains challenging. Multiscale entropy (MSE) derived ...

Human-Guided Agentic AI for Multimodal Clinical Prediction: Lessons from the AgentDS Healthcare Benchmark

Agentic AI systems are increasingly capable of autonomous data science workflows, yet clinical prediction tasks demand domain expertise that purely au...

Facial photographs reveal mortality risk beyond triage

Rapid risk stratification is essential in the clinic, yet vital signs, laboratory tests, and triage scores may not fully capture risk at presentation....

Boards-style benchmarks overestimate prior-chat bias in large language models: a factorial evaluation study

Background: Large language models (LLMs) are increasingly piloted as chat interfaces for chart review and clinical decision support. Although leading ...

Safety and Utility of an Agentic Large Language Model-Based Hospital Course Summarizer: A Prospective Real-World Pilot Study

Importance: High-quality discharge summaries are essential for safe care transitions but contribute substantially to clinician documentation burden an...

Improving mortality prediction in critically ill cancer patients with a multidimensional machine learning model

Background: Prognostic assessment in critically ill patients with cancer remains challenging, as conventional ICU severity scores often perform subopt...

Efficient Variance-reduced Estimation from Generative EHR Models: The SCOPE and REACH Estimators

Generative models trained using self-supervision of tokenized electronic health record (EHR) timelines show promise for clinical outcome prediction. T...

UniPACT: A Multimodal Framework for Prognostic Question Answering on Raw ECG and Structured EHR

Accurate clinical prognosis requires synthesizing structured Electronic Health Records (EHRs) with real-time physiological signals like the Electrocar...

Association of Deep Learning-Derived Temporalis Sarcopenia with Mortality in Acute Ischemic Stroke

Background: Sarcopenia is associated with mortality and morbidity following acute ischemic stroke (AIS), but the diagnosis requires specialized equipm...

Attention-Based Offline Reinforcement Learning and Clustering for Interpretable Sepsis Treatment

Sepsis remains one of the leading causes of mortality in intensive care units, where timely and accurate treatment decisions can significantly impact ...

Achieving Expert-Level Clinical Infection Detection with LLMs from Clinical Documents: Validation in Complex Patient Cases with Cirrhosis

BackgroundSystemic infections are a leading cause of hospitalization and death among patients with cirrhosis. Timely and accurate infection identifica...

Advances in sulfate-reducing bacteria-driven bioelectrolysis: mechanisms and applications in microbial electrolysis cell technology.

The discharge of sulfate-rich wastewater from chemical and pharmaceutical and food processing industries results in serious environmental problems tha...

RIPTOSO: The development of a screening tool for adverse events during forensic-psychiatric inpatient treatments of offenders with schizophrenia spectrum disorders.

Adverse events such as compulsory measures, absconding, illicit substance use, self-harm, aggressive behavior, and prolonged hospitalization pose sign...

Affective-ROPTester: Capability and Bias Analysis of LLMs in Predicting Retinopathy of Prematurity

Despite the remarkable progress of large language models (LLMs) across various domains, their capacity to predict retinopathy of prematurity (ROP) r...

LCDS: A Logic-Controlled Discharge Summary Generation System Supporting Source Attribution and Expert Review

Despite the remarkable performance of Large Language Models (LLMs) in automated discharge summary generation, they still suffer from hallucination i...