Hospital-Based Medicine

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

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A Bilingual On-premise AI agent for Clinical Drafting: Seamless EHR integration in the Y-KNOT Project

Large Language Models (LLMs) have shown promise in reducing clinical documentation burden, yet their...

Optimized BERT-based NLP outperforms Zero-Shot Methods for Automated Symptom Detection in Clinical Practice

Large Language Nodels (LLMs) have raised broad expectations for clinical use, particularly in the pr...

Comparing Machine Learning and Nurse Predictions for Hospital Admissions in a Multisite Emergency Care System

Emergency department (ED) crowding strains patient care and drives up costs. Early decisions on the ...

Machine learning-based short-term forecasting of COVID-19 hospital admissions using routine hospital patient data

During the COVID-19 pandemic, the field of infectious disease modeling advanced rapidly, with foreca...

Retrospective Machine Learning Approach for Forecasting In-Hospital Death in ICU Patients After Cardiac Arrest

Accurate identification of patients at high risk of in-hospital mortality in intensive care units (I...

Beyond episodic early warning systems: a continuous clinical alert system for early detection of in-hospital deterioration

Efficient patient monitoring on the medical-surgical wards is crucial to prevent significant in-hosp...

Changes in psychiatric documentation and treatment in primary care with artificial intelligence scribe use

Despite increasingly widespread use of artificial intelligence-driven ambient scribes in medicine, t...

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 composi...

Large-Language-Model Mortality Risk Stratification in the Intensive Care Unit: A Benchmark Against APACHE II

Accurately predicting clinical trajectories in critically ill patients remains challenging due to ph...

AI-MI: A Deep Learning Model to Predict Actionable Acute Coronary Syndrome Using 12-Lead ECGs

Chest pain is among the most common chief complaints in Emergency Departments (EDs), and differentia...

Large-scale Local Deployment of DeepSeek-R1 in Pilot Hospitals in China: A Nationwide Cross-sectional Survey

The open-source release of DeepSeek-R1, a high-performing large language model (LLM), enables local ...

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 p...

Assessment of the Modified Rankin Scale in Electronic Health Records with a Fine-tuned Large Language Model

The modified Rankin scale (mRS) is an important metric in stroke research, often used as a primary o...

Interictal Epileptiform Discharge Detection Using Probabilistic Diffusion Models and AUPRC Maximization

Recently, automated Interictal Epileptiform Discharge (IED) detection has attracted significant atte...

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 st...

Leveraging neighborhood-level Information to Improve Model Fairness in Predicting Prenatal Depression

Perinatal depression (PND) affects 10-20% of pregnant women, with significant racial disparities in ...

AI Implementation in U.S. Healthcare and Its Association With Elder Mortality and Quality of Care

Hospitals are increasingly adopting artificial intelligence (AI) tools in clinical care. However, th...

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