Hospital-Based Medicine

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

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Prediction of 30-Day Morbidity and Mortality After Conversion of Sleeve Gastrectomy to Roux-en-Y Gastric Bypass: Use of an Artificial Neural Network.

BACKGROUND: Conversion of sleeve gastrectomy to Roux-en-Y gastric bypass is indicated primarily for ...

Reliability of large language models in managing odontogenic sinusitis clinical scenarios: a preliminary multidisciplinary evaluation.

PURPOSE: This study aimed to evaluate the utility of large language model (LLM) artificial intellige...

An audit of medullary thyroid carcinoma from a tertiary care hospital in northwest India.

INTRODUCTION: Medullary thyroid carcinoma (MTC) is a rare thyroid malignancy originating from parafo...

Evaluation of different machine learning algorithms for predicting the length of stay in the emergency departments: a single-centre study.

BACKGROUND: Recently, crowding in emergency departments (EDs) has become a recognised critical facto...

Easing the Burden on Caregivers- Applications of Artificial Intelligence for Physicians and Caregivers of Children with Cleft Lip and Palate.

ObjectiveMany caregivers of children with cleft lip and palate experience a high level of anxiety th...

A personalized prediction model for urinary tract infections in type 2 diabetes mellitus using machine learning.

Patients with type 2 diabetes mellitus (T2DM) are at higher risk for urinary tract infections (UTIs)...

Predicting the risk of hospital readmissions using a machine learning approach: a case study on patients undergoing skin procedures.

INTRODUCTION: Even with modern advancements in medical care, one of the persistent challenges hospit...

How satisfied are patients with nursing care and why? A comprehensive study based on social media and opinion mining.

To assess the overall experience of a patient in a hospital, many factors must be analyzed; nonethel...

Machine learning hypothesis-generation for patient stratification and target discovery in rare disease: our experience with Open Science in ALS.

INTRODUCTION: Advances in machine learning (ML) methodologies, combined with multidisciplinary colla...

The clinical course of hospitalized COVID-19 patients and aggravation risk prediction models: a retrospective, multi-center Korean cohort study.

BACKGROUND: Understanding the clinical course and pivotal time points of COVID-19 aggravation is cri...

Current Status and Future Directions: The Application of Artificial Intelligence/Machine Learning for Precision Medicine.

Technological innovations, such as artificial intelligence (AI) and machine learning (ML), have the ...

Deep learning-based prediction of in-hospital mortality for sepsis.

As a serious blood infection disease, sepsis is characterized by a high mortality risk and many comp...

Application of Machine Learning Techniques to Development of Emergency Medical Rapid Triage Prediction Models in Acute Care.

Given the critical and complex features of medical emergencies, it is essential to develop models th...

The correlation between serum creatinine and burn severity and its predictive value.

This study aimed to explore the correlation between serum creatinine and burn severity and the value...

Pregnancy-associated asymptomatic bacteriuria and antibiotic resistance in the Maternity and Children's Hospital, Arar, Saudi Arabia.

INTRODUCTION: The Ministry of Health in Saudi Arabia provides comprehensive antenatal care for all p...

Personalized Predictive Hemodynamic Management for Gynecologic Oncologic Surgery: Feasibility of Cost-Benefit Derivatives of Digital Medical Devices.

BACKGROUND: Intraoperative hypotension is associated with increased perioperative complications, hos...

Preoperative Delirium Risk Screening in Patients Undergoing a Cardiac Surgery: Results from the Prospective Observational FINDERI Study.

OBJECTIVE: Postoperative delirium (POD) is a common complication of cardiac surgery that is associat...

Investigating Activity Recognition for Hemiparetic Stroke Patients Using Wearable Sensors: A Deep Learning Approach with Data Augmentation.

Measuring the daily use of an affected limb after hospital discharge is crucial for hemiparetic stro...

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