Infection Control

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 enhancing patient care, managing hospital resources, and optimizing the use of patient beds. Therefore, this study aimed to predict the LOS for patients hospitalized in various clinics using different artificial intelligence (AI) models.

Development and validation of a machine learning-based nomogram for predicting prognosis in lung cancer patients with malignant pleural effusion.

Malignant pleural effusion (MPE) is a common complication in patients with advanced lung cancer, significantly impacting their survival rates and quality of life. Effective tools for assessing the prognosis of these patients are urgently needed to enable early intervention. This study retrospectively analyzed patient data from the Affiliated Hospital of North Sichuan Medical College from 2013 to 2...

Development and validation of machine learning-based prediction model for outcome of cardiac arrest in intensive care units.

Cardiac arrest (CA) poses a significant global health challenge and often results in poor prognosis. We developed an interpretable and applicable mach...

Leveraging Artificial Intelligence to Reduce Neuroscience ICU Length of Stay.

GOAL: Efficient patient flow is critical at Tampa General Hospital (TGH), a large academic tertiary care center and safety net hospital with more than...

Automatic detecting multiple bone metastases in breast cancer using deep learning based on low-resolution bone scan images.

Whole-body bone scan (WBS) is usually used as the effective diagnostic method for early-stage and comprehensive bone metastases of breast cancer. WBS ...

AI-augmented Biophysical modeling in thermoplasmonics for real-time monitoring and diagnosis of human tissue infections.

Identifying tissue infections from the body still poses an unprecedented challenge in society. Conventional diagnostic procedures are time-consuming a...

Developing practical machine learning survival models to identify high-risk patients for in-hospital mortality following traumatic brain injury.

Machine learning (ML) offers precise predictions and could improve patient care, potentially replacing traditional scoring systems. A retrospective st...

Social Determinants and Health Equity Activities: Are They Connected with the Adaptation of AI and Telehealth Services in the U.S. Hospitals?

In recent decades, technological shifts within the healthcare sector have significantly transformed healthcare management and utilization, introducing...

Artificial Intelligence Model for Detection of Colorectal Cancer on Routine Abdominopelvic CT Examinations: A Training and External-Testing Study.

Radiologists are prone to missing some colorectal cancers (CRCs) on routine abdominopelvic CT examinations that are in fact detectable on the images....

Sharing reliable information worldwide: healthcare strategies based on artificial intelligence need external validation. Position paper.

Training machine learning models using data from severe COVID-19 patients admitted to a central hospital, where entire wards are specifically dedicate...

Utilizing machine learning to predict hospital admissions for pediatric COVID-19 patients (PrepCOVID-Machine).

The COVID-19 pandemic has burdened healthcare systems globally. To curb high hospital admission rates, only patients with genuine medical needs are ad...

Can artificial intelligence lower the global sudden cardiac death rate? A narrative review.

PURPOSE OF REVIEW: WHO defines SCD as sudden unexpected death either within 1 h of symptom onset (witnessed) or within 24 h of having been observed al...

Robotic versus manual disinfection of global priority pathogens at COVID-19-dedicated hospitals.

BACKGROUND: Twelve bacterial families identified as global priority pathogens (GPPs) pose the greatest threat to human health due to declining antibio...

Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence-Assisted Cancer Diagnosis.

The potential of artificial intelligence (AI) in digital pathology is limited by technical inconsistencies in the production of whole slide images (WS...

Using Inertial Measurement Units and Machine Learning to Classify Body Positions of Adults in a Hospital Bed.

In hospitals, timely interventions can prevent avoidable clinical deterioration. Early recognition of deterioration is vital to stopping further decli...

Multimodal deep learning for predicting in-hospital mortality in heart failure patients using longitudinal chest X-rays and electronic health records.

Amid an aging global population, heart failure has become a leading cause of hospitalization among older people. Its high prevalence and mortality rat...

Novel silver nanoparticle-based biomaterials for combating biofilms.

BACKGROUND: is a significant nosocomial pathogen that has developed resistance to multiple antibiotics, often forming biofilms that enhance its virul...

External validation of an artificial intelligence model using clinical variables, including ICD-10 codes, for predicting in-hospital mortality among trauma patients: a multicenter retrospective cohort study.

Artificial intelligence (AI) is being increasingly applied in healthcare to improve patient care and clinical outcomes. We previously developed an AI ...

Hope for the best prepare for the worst: acute kidney disease and catastrophic comorbidities (a case report).

It is evident that Acute Kidney Injury (AKI) is an independent risk factor for both the survival of patients and their kidneys. Here, we present a cas...

Hypothesis generation for rare and undiagnosed diseases through clustering and classifying time-versioned biological ontologies.

Rare diseases affect 1-in-10 people in the United States and despite increased genetic testing, up to half never receive a diagnosis. Even when using ...