Pediatrics

How artificial intelligence could transform emergency care.

Artificial intelligence (AI) in healthcare is the ability of a computer to perform tasks typically a...

Development and Internal Validation of a Multivariable Prediction Model for Mortality After Hip Fracture with Machine Learning Techniques.

In order to estimate the likelihood of 1, 3, 6 and 12 month mortality in patients with hip fractures...

Exploring the potential of machine learning in gynecological care: a review.

Gynecological health remains a critical aspect of women's overall well-being, with profound implicat...

Predictive modeling for early detection of biliary atresia in infants with cholestasis: Insights from a machine learning study.

Cholestasis, characterized by the obstruction of bile flow, poses a significant concern in neonates ...

Sentiment analysis of Indonesian tweets on COVID-19 and COVID-19 vaccinations.

BACKGROUND: Sentiments and opinions regarding COVID-19 and the COVID-19 vaccination on Indonesian-la...

Reply to: "Addressing Chatbots as Artificial Intelligence Aids in Pediatric Pathology".

Single-port robotic-assisted laparoscopic synchronous surgery in pediatric patent processus vaginalis.

PURPOSE: Patent processus vaginalis (PPV) is usually observed in pediatric abdominal surgery; howeve...

Machine learning for enhanced prognostication: predicting 30-day outcomes following posterior fossa decompression surgery for Chiari malformation type I in a pediatric cohort.

OBJECTIVE: Chiari malformation type I (CM-I) is a congenital disorder occurring in 0.1% of the popul...

Development and validation of machine learning models to predict frailty risk for elderly.

AIMS: Early identification and intervention of the frailty of the elderly will help lighten the burd...

Machine learning-assisted chromium speciation using a single-well ratiometric fluorescent nanoprobe.

Chromium is widely recognized as a significant pollutant discharged into the environment by various ...

Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review.

Reinforcement learning (RL) has emerged as a dynamic and transformative paradigm in artificial intel...

Towards quantifying biomarkers for respiratory distress in preterm infants: Machine learning on mid infrared spectroscopy of lipid mixtures.

Neonatal respiratory distress syndrome (nRDS) is a challenging condition to diagnose which can lead ...

AI in radiology: Legal responsibilities and the car paradox.

The integration of AI in radiology raises significant legal questions about responsibility for error...

Novel opportunities for clinical pharmacy research: development of a machine learning model to identify medication related causes of delirium in different patient groups.

The advent of artificial intelligence (AI) technologies has taken the world of science by storm in 2...

Using Social Media and Artificial Intelligence to Find Your Purpose.

These days, the distractions of the digital world often obscure the journey to discover one's purpos...

Applying Machine Learning for Enhanced MicroRNA Analysis: A Companion Risk Tool for Oral Squamous Cell Carcinoma in Standard Care Incisional Biopsy.

Machine learning analyses within the realm of oral cancer outcomes are relatively underexplored comp...

Moral Engagement and Disengagement in Health Care AI Development.

BACKGROUND: Machine learning (ML) is utilized increasingly in health care, and can pose harms to pat...

Prediction of polyspecificity from antibody sequence data by machine learning.

Antibodies are generated with great diversity in nature resulting in a set of molecules, each optimi...

Fully automated deep learning approach to dental development assessment in panoramic radiographs.

BACKGROUND: Dental development assessment is an important factor in dental age estimation and dental...

Algor-ethics: charting the ethical path for AI in critical care.

The integration of Clinical Decision Support Systems (CDSS) based on artificial intelligence (AI) in...

Identifying pediatric heart murmurs and distinguishing innocent from pathologic using deep learning.

OBJECTIVE: To develop a deep learning algorithm to perform multi-class classification of normal pedi...