Hospital-Based Medicine

Improving Phenotyping of Patients With Immune-Mediated Inflammatory Diseases Through Automated Processing of Discharge Summaries: Multicenter Cohort Study.

BACKGROUND: Valuable insights gathered by clinicians during their inquiries and documented in textual reports are often unavailable in the structured data recorded in electronic health records (EHRs).

Evaluation of machine learning methods for prediction of heart failure mortality and readmission: meta-analysis.

BACKGROUND: Heart failure (HF) impacts nearly 6 million individuals in the U.S., with a projected 46% increase by 2030, is creating significant healthcare burdens. Predictive models, particularly machine learning (ML)-based models, offer promising solutions to identify patients at greater risk of adverse outcomes, such as mortality and hospital readmission. This review aims to assess the effective...

Transformer-based deep learning ensemble framework predicts autism spectrum disorder using health administrative and birth registry data.

Early diagnosis and access to resources, support and therapy are critical for improving long-term outcomes for children with autism spectrum disorder ...

Clinical implementation of AI-based screening for risk for opioid use disorder in hospitalized adults.

Adults with opioid use disorder (OUD) are at increased risk for opioid-related complications and repeated hospital admissions. Routine screening for p...

Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial.

The COmmunicating Narrative Concerns Entered by RNs (CONCERN) early warning system (EWS) uses real-time nursing surveillance documentation patterns in...

Enhanced prediction of ventilator-associated pneumonia in patients with traumatic brain injury using advanced machine learning techniques.

Ventilator-associated pneumonia significantly increases morbidity, mortality, and healthcare costs among patients with traumatic brain injury. Accurat...

Beyond the Mirror: Body Dysmorphic Disorder and Emerging Dysmorphias in Aesthetic Surgery.

Driven by social media and artificial intelligence technologies, new dysmorphias increase pressures on body image, often leading patients to pursue re...

Effectiveness of an intelligent weight-bearing rehabilitation robot in enhancing recovery following anterior cruciate ligament reconstruction.

AIM: Orthopedic surgery patients frequently delay early rehabilitation due to postoperative discomfort. This is especially true for younger patients w...

Benchmarking of Large Language Models for the Dental Admission Test.

Large language models (LLMs) have shown promise in educational applications, but their performance on high-stakes admissions tests, such as the Denta...

Machine learning-driven prediction of hospital admissions using gradient boosting and GPT-2.

BACKGROUND: Accurately predicting hospital admissions from the emergency department (ED) is essential for improving patient care and resource allocati...

Development and validation of inpatient mortality prediction models for patients with hyperglycemic crisis using machine learning approaches.

BACKGROUND: Hyperglycemic crisis is one of the most common and severe complications of diabetes mellitus, associated with a high motarlity rate. Emerg...

A comparative study of neuro-fuzzy and neural network models in predicting length of stay in university hospital.

BACKGROUND: The time a patient spends in the hospital from admission to discharge is known as the length of stay (LOS). Predicting LOS is crucial for ...

Explainable machine learning to identify risk factors for unplanned hospital readmissions in Nova Scotian hospitals.

OBJECTIVE: A report from the Canadian Institute for Health Information found unplanned hospital readmissions (UHR) common, costly, and potentially avo...

Traumatic brain injury: Bridging pathophysiological insights and precision treatment strategies.

Blood-brain barrier disruption and the neuroinflammatory response are significant pathological features that critically influence disease progression ...

Post-Anesthesia Care Unit (PACU) readiness predictions using machine learning: a comparative study of algorithms.

INTRODUCTION: Accurate and timely discharge from the Post-Anesthesia Care Unit (PACU) is essential to prevent postoperative complications and optimize...

Assessing the accuracy of the GPT-4 model in multidisciplinary tumor board decision prediction.

PURPOSE: Artificial intelligence models like GPT-4 (OpenAI) have the potential to support clinical decision-making in oncology. This study aimed to as...

Optimizing imaging modalities for sarcoma subtypes in radiation therapy: State of the art.

The choice of imaging modalities is essential in sarcoma management, as different techniques provide complementary information depending on tumor subt...

Perceived Barriers and Facilitators of Use of Artificial Intelligence in Eating Disorder Care: A Commentary on Linardon et al. (2025).

Artificial intelligence (AI) has the potential to revolutionize mental health care, including for eating disorders, but there are still a number of co...

Evaluation and comparison of machine learning algorithms for predicting discharge against medical advice in injured inpatients.

BACKGROUND: Whether the application of machine learning algorithms offers an advantage over logistic regression in forecasting discharge against medic...

Explainable SHAP-XGBoost models for pressure injuries among patients requiring with mechanical ventilation in intensive care unit.

pressure injuries are significant concern for ICU patients on mechanical ventilation. Early prediction is crucial for enhancing patient outcomes and r...