Hospital-Based Medicine

Identifying Key Predictors of Appropriate Discharge Destinations for Older Inpatients in Acute Care: Scoping Review.

BACKGROUND: Postacute care (PAC) services are important to ensure functional recovery and provide adequate care for geriatric inpatients in acute care. The choice between different PAC options can be challenging, and predictors for the most appropriate among diverse discharge options are warranted. OBJECTIVE: We conducted a scoping review to identify predictors of appropriate discharge destination...

Machine learning for predicting functional outcomes in acute ischemic stroke: insights from a nationwide stroke registry.

Accurately predicting the prognosis of patients with acute ischemic stroke at discharge remains highly challenging after active treatment. The aim of this retrospective nationwide registry-based study was to identify key predictors associated with favorable outcomes and to develop machine learning models for patient outcome prediction. Analysis of a comprehensive dataset of 40,586 patients reveale...

The efficiency paradox of discharge masking head loss in run-of-river hydropower generation.

While climate impacts on hydropower output are well-documented, plant efficiency, the critical ratio of electrical energy generated to hydraulic energ...

A hierarchical fusion framework for vehicle to grid energy management using predictive intelligence and learning based pricing.

One potential remedy for grid stability and energy efficiency is the integration of electric vehicles (EVs) into the grid via Vehicle-to-Grid (V2G) te...

Prehospital transfusion strategies in haemorrhagic shock.

BACKGROUND: Massive transfusion protocols are established in-hospital practices for managing haemorrhagic shock, yet critical bleeding accounts for up...

Assessing in-hospital mortality risk in ICU lung cancer patients using machine learning: An analysis based on the MIMIC-IV database.

BACKGROUND: Patients with advanced lung cancer admitted to the intensive care unit (ICU) face a substantially elevated risk of in-hospital mortality. ...

"I Double Checked It with My Own Knowledge:" Physician Perspectives on the Use of AI Chatbots for Clinical Decision-Making.

BACKGROUND: AI chatbots are proliferating in healthcare systems. It is essential to explore how physicians use these tools in order to understand thei...

The effect of AI-assisted bedside echocardiography on inpatient care: a prospective trial.

AIMS: Focused cardiac ultrasound (FoCUS) can yield valuable information for decision-making. However, it is limited by the skills required to acquire ...

Psychiatric Documentation and Management in Primary Care With Artificial Intelligence Scribe Use.

IMPORTANCE: Despite increasingly widespread use of artificial intelligence (AI)-driven ambient scribes in medicine, the extent to which they are assoc...

Dual Ontology-enhanced Clinical Decision Learning for First-admission Mortality Prediction.

Time-series based deep learning methods have significantly improved performance of predictive healthcare tasks on electronic health records (EHR) data...

Multidisciplinary artificial intelligence systems versus single-model approaches for the diagnosis and management of ileus and volvulus.

BACKGROUND AND AIMS: The accurate and timely diagnosis of ileus versus volvulus is essential in emergency care, as treatment choices directly influenc...

Multidimensional analysis of clinical pharmacist intervention impact on hospital length of stay in pulmonary tuberculosis: a random forest-driven retrospective study.

[Predictive value of a multimodal radiomics model for central lymph node metastasis in clinically node-negative papillary thyroid microcarcinoma based on machine learning].

OBJECTIVES: To develop and validate a multimodal radiomics model based on machine learning for predicting central lymph node metastasis (CLNM) in pati...

Contrastive learning enhanced retrieval-augmented few-shot framework for multi-label patent classification.

The rapid expansion of patent databases poses increasing challenges for multi-label patent classification, particularly for inventions spanning multip...

Development and validation of an Interpretable Machine learning model for Discriminating between benign and malignant breast cancer.

OBJECTIVE: Breast cancer prognosis depends on early detection. We developed and externally validated a model using routine, readily available clinical...

Integrating Artificial Intelligence in Nursing Practice With Decubitus Risk Prediction Alerts: A Pilot Process Evaluation.

AIMS: To evaluate the acceptability and feasibility among nurses of Decubitus Risk Prediction Alerts based on Artificial Intelligence (DRAAI), and to ...

Goals of Care Transitions following Postoperative Undertriage.

BACKGROUND: This study tests the hypothesis that postoperative undertriage of high-acuity patients to hospital floor units is associated with new post...

Artificial Intelligence Competencies and Educational Needs Among ERNICA Members: Results of a Multinational Survey.

INTRODUCTION: Artificial Intelligence (AI) is increasingly recognized as a transformative force in healthcare. In the field of rare diseases, AI can e...

An artificial intelligence-powered learning health system to improve sepsis detection and quality of care: a before-and-after study.

Sepsis is a major global health crisis where early recognition and effective management remain significant challenges for healthcare systems. As part ...

Machine learning-based risk modeling for safety-focused learning curve assessment in robotic left-sided colorectal cancer surgery.

The transition from laparoscopic to robotic surgery for left-sided colorectal cancer raises safety concerns during the learning curve, particularly wh...