Hospital-Based Medicine

Artificial Intelligence Monitoring of Neurological Status From Patient Videos in the Neuroscience Intensive Care Unit.

BACKGROUND AND OBJECTIVES: The neurological examination is pivotal in assessing patients with neurological conditions but has severe limitations: It can vary between examiners, may not discern subtle or subacute changes, and being intermittent can delay recognition of new deficits. Most intensive care units/hospitals lack subspecialized neurocritical care services, exacerbating these problems. We ...

Dynamic Ensemble Selection for Early Detection of Deep Vein Thrombosis in Fracture Patients.

Deep vein thrombosis (DVT) in fracture patients is often clinically silent, with a high incidence of thrombosis and associated mortality. Static machine learning methods struggle to address the challenge of early DVT diagnosis due to their inability to adapt to heterogeneous data across patients. In contrast, Dynamic Ensemble Selection (DES) improves clinical decision-making and therapeutic interv...

ChatGPT-guided translation of pediatric emergency department discharge instructions.

BACKGROUND: Language barriers in pediatric emergency medicine discharge instructions can impact patient safety, leading to poorer post-discharge outco...

Risk Factors and Prediction of Chronic Postsurgical Pain Among Patients With Distal Lower Extremity Fracture: Cohort Analysis.

BACKGROUND: The surgical interventions aimed at fracture repair are often accompanied by chronic postsurgical pain (CPSP), which is associated with de...

Initial diagnosis and management of acute ischemic stroke: recent update and future direction.

Acute ischemic stroke (AIS) outcomes depend critically on rapid, accurate early diagnosis in the emergency department. Traditional prehospital tools a...

Development and deployment of an interpretable stacking ensemble model for predicting in-hospital mortality in ICU patients with chronic kidney disease and sepsis.

OBJECTIVE: To develop an interpretable stacking ensemble model for predicting in-hospital mortality in intensive care unit (ICU) patients with CKD and...

Communicable diseases platform (CDP): Real-Time clinical analytics for infections.

OBJECTIVE: Communicable diseases, especially seasonal respiratory illnesses, contribute significantly to paediatric hospital presentations and admissi...

Development of a new tool for prediction of hospital length of stay and intensive care needs in trauma patients using Machine Learning.

BACKGROUND: Trauma is a major global health burden leading to significant morbidity, disability, and mortality. Predictive models in trauma care tradi...

Preliminary insights into artificial intelligence guided dosing in hypertension and diabetes: challenges and lessons learnt in a pilot feasibility study.

OBJECTIVE: CURATE.AI is an artificial intelligence platform enabling personalised drug dosing. Aims:1) Determine the feasibility of using CURATE.AI in...

Multicenter Evaluation of an Artificial Intelligence System for Automatic Recognition of Fetal Ultrasound Findings Suggestive of Congenital Malformations.

OBJECTIVE: To evaluate the diagnostic performance of an artificial intelligence (AI) system for detecting eight abnormal fetal ultrasound findings acr...

Sustainable bioenergy manufacturing in plants.

Sustainable bioenergy is pivotal to the global transition from fossil fuels to a circular bioeconomy. However, conventional biomass conversion remains...

Predictors of Outcome Clusters in Patients With Unruptured Intracranial Aneurysms Treated With Microsurgery: An Unsupervised Machine Learning Analysis.

BACKGROUND AND OBJECTIVES: Identifying surgical candidates who are prone to poor outcomes is crucial for adapting treatment and ensuring optimal outco...

Effects of a transformer-based AI-based application to support incontinence-associated dermatitis and pressure injury assessment, nursing care and documentation: Controlled pilot intervention study.

BACKGROUND: Artificial intelligence (AI) is playing an increasingly important role in nursing care, including wound management. Differentiating pressu...

Predicting activities of daily living at discharge in stroke patients using rehabilitation robot training-induced functional connectivity.

BACKGROUND: Predicting activities of daily living (ADL) in stroke patients optimizes discharge planning, which relies on accurate functional assessmen...

Association between the platelet to white blood cell ratio and short term mortality in critically ill patients with atherosclerotic cardiovascular disease: A retrospective study and machine learning with external validation.

BACKGROUND: The platelet to white blood cell ratio (PWR) has shown prognostic value in many diseases. Yet its predictive utility for patients with ath...

Artificial intelligence for personalized management of vestibular schwannoma: a multidisciplinary clinical implementation study.

OBJECTIVES: Management of patients with vestibular schwannoma (VS) relies on precise tumor size and growth trend evaluation. We introduce and evaluate...

Discovery of PPARγ Partial Agonists for Treatment of Type 2 Diabetes Based on an Integrated Virtual Screening Strategy that Combines Fragment Molecular Orbital Calculations, Machine Learning, Molecular Docking, Interaction Fingerprint Filtering, and Molecular Dynamics Simulations.

Peroxisome proliferator-activated receptor γ (PPARγ) is a key therapeutic target for type 2 diabetes and cardiovascular diseases due to its central ro...

Potential change in healthcare costs of implementing artificial intelligence for detecting cervical spine fractures on CT: an early health technology assessment.

OBJECTIVES: To assess healthcare costs of patients screened for cervical spine (C-spine) fractures using CT, and estimate the change in in-hospital co...

Is chronic low back pain related to sleep disturbances? A self-organizing maps for unsupervised machine learning-based patient phenotyping.

BACKGROUND CONTEXT: Chronic low back pain (CLBP) is a multifactorial condition and a leading cause of disability worldwide. Among the various contribu...

Clinical significance of machine learning algorithm in predicting PPM during TAVR in small annuli.

Compared with left heart catheterization (LHC), the pressure gradient of an aortic valve (PGAV) measured by echocardiography during transcatheter aort...