Pediatrics

Predicting the risk of asthma attacks in children, adolescents and adults: protocol for a machine learning algorithm derived from a primary care-based retrospective cohort.

INTRODUCTION: Most asthma attacks and subsequent deaths are potentially preventable. We aim to develop a prognostic tool for identifying patients at high risk of asthma attacks in primary care by leveraging advances in machine learning.

Indoor location identification of patients for directing virtual care: An AI approach using machine learning and knowledge-based methods.

In a digitally enabled healthcare setting, we posit that an individual's current location is pivotal for supporting many virtual care services-such as tailoring educational content towards an individual's current location, and, hence, current stage in an acute care process; improving activity recognition for supporting self-management in a home-based setting; and guiding individuals with cognitive...

Feed-forward neural networks using cerebral MR spectroscopy and DTI might predict neurodevelopmental outcome in preterm neonates.

OBJECTIVES: We aimed to evaluate the ability of feed-forward neural networks (fNNs) to predict the neurodevelopmental outcome (NDO) of very preterm ne...

The understanding of congruent and incongruent referential gaze in 17-month-old infants: an eye-tracking study comparing human and robot.

Several studies have shown that the human gaze, but not the robot gaze, has significant effects on infant social cognition and facilitate social engag...

Effect of Missing Data Imputation on Deep Learning Prediction Performance for Vesicoureteral Reflux and Recurrent Urinary Tract Infection Clinical Study.

Missing observations are always a challenging problem that we have to deal with in diseases that require follow-up. In hospital records for vesicouret...

Predicting the Risk of Adverse Events in Pregnant Women With Congenital Heart Disease.

Background Women with congenital heart disease are considered at high risk for adverse events. Therefore, we aim to establish 2 prediction models for ...

Using Topic Modeling Methods for Short-Text Data: A Comparative Analysis.

With the growth of online social network platforms and applications, large amounts of textual user-generated content are created daily in the form of ...

Public Perception of Artificial Intelligence in Medical Care: Content Analysis of Social Media.

BACKGROUND: High-quality medical resources are in high demand worldwide, and the application of artificial intelligence (AI) in medical care may help ...

Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data.

Over the past years, the assisted reproductive technologies (ARTs) have been accompanied by constant innovations. For instance, intracytoplasmic sperm...

Lio-A Personal Robot Assistant for Human-Robot Interaction and Care Applications.

Lio is a mobile robot platform with a multi-functional arm explicitly designed for human-robot interaction and personal care assistant tasks. The robo...

Fatal case of hospital-acquired hypernatraemia in a neonate: lessons learned from a tragic error.

A 3-week-old boy with viral gastroenteritis was by error given 200 mL 1 mmol/mL hypertonic saline intravenously instead of isotonic saline. His plasma...

Aptamer based proteomic pilot study reveals a urine signature indicative of pediatric urinary tract infections.

OBJECTIVE: Current urinary tract infection (UTI) diagnostic strategies that rely on leukocyte esterase have limited accuracy. We performed an aptamer-...

Machine learning for pattern detection in cochlear implant FDA adverse event reports.

Medical device performance and safety databases can be analyzed for patterns and novel opportunities for improving patient safety and/or device desig...

Benchmarking machine learning models on multi-centre eICU critical care dataset.

Progress of machine learning in critical care has been difficult to track, in part due to absence of public benchmarks. Other fields of research (such...

Artificial Intelligence and Robotics in Nursing: Ethics of Caring as a Guide to Dividing Tasks Between AI and Humans.

Nurses have traditionally been regarded as clinicians that deliver compassionate, safe, and empathetic health care (Nurses again outpace other profess...

Semantic segmentation of cerebrospinal fluid and brain volume with a convolutional neural network in pediatric hydrocephalus-transfer learning from existing algorithms.

BACKGROUND: For the segmentation of medical imaging data, a multitude of precise but very specific algorithms exist. In previous studies, we investiga...

Neonatal EEG sleep stage classification based on deep learning and HMM.

OBJECTIVE: Automatic sleep stage scoring is of great importance for investigating sleep architecture during infancy. In this work, we introduce a nove...

Artificial intelligence in health care: laying the Foundation for Responsible, sustainable, and inclusive innovation in low- and middle-income countries.

The World Health Organization and other institutions are considering Artificial Intelligence (AI) as a technology that can potentially address some he...

Artificial Intelligence and Big Data in Diabetes Care: A Position Statement of the Italian Association of Medical Diabetologists.

Since the last decade, most of our daily activities have become digital. Digital health takes into account the ever-increasing synergy between advance...

Robot-assisted laparoscopic diverticulectomy with ureteral reimplantation.

OBJECTIVE: The objective is to describe our experience with robot-assisted laparoscopic diverticulectomy with extravesical ureteral reimplantation in ...