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Machine Learning

Uncovering Racial and Ethnic Disparities in Pediatric Sleep Research
 Uncovering Racial and Ethnic Disparities in Pediatric Sleep Research 1024 683 Erin Gregory
By: Erin Gregory

In a recent study published in the Journal of Biomedical Informatics, Mattina Davenport, PhD, principal investigator in the Center for Child Health Equity and Outcomes Research at the Abigail Wexner Research Institute at Nationwide Children’s Hospital, explores the impact of biases in clinical documentation on pediatric sleep research. The Motivation Behind the Study Pediatric sleep…

Demystifying Machine Learning With AutoML
 Demystifying Machine Learning With AutoML 1024 683 Abbie Miller
By: Abbie Miller

A Nationwide Children’s Hospital team from the Office of Data Sciences recently won the Advanced ML tier in the precisionFDA Automated Machine Learning (AutoML) App-a-thon Challenge: Democratizing and Demystifying Artificial Intelligence (AI). Each year, the FDA hosts challenges centered around data science and bioinformatics. This year, the challenge focuses on AutoML, a low-code ML technique…

New Approach to Understanding Slow Oscillations in the Sleeping Brain
 New Approach to Understanding Slow Oscillations in the Sleeping Brain 150 150 Jessica Nye, PhD
By: Jessica Nye, PhD

Investigators have proposed a novel model-based approach that leverages data generated during sleep to mimic global slow oscillations in the sleeping brain with closed-loop (cl) Transcranial Alternating Current Stimulation (tACS). “We’re really interested in the sleeping brain because as we develop, the brain is changing. During sleep, we process information that we learn during the…

Enhancing Intestinal Rehabilitation Workflow with Disease-Specific Documentation Tools
 Enhancing Intestinal Rehabilitation Workflow with Disease-Specific Documentation Tools 150 150 Erin Gregory
By: Erin Gregory

Structured data entry not only reduces the amount of time physicians are spending in the electronic health record but also opens the door for new research. A recent study published in JAMA Pediatrics by Nationwide Children’s Hospital’s Ethan Mezoff, MD, Jennifer Lee, MD, and team has shed light on a promising solution to improve the…

Using Machine Learning to Classify Treatment Approaches for Infants with Bronchopulmonary Dysplasia
 Using Machine Learning to Classify Treatment Approaches for Infants with Bronchopulmonary Dysplasia 1024 683 Pam Georgiana
By: Pam Georgiana

We don’t yet know the best way to help infants with severe bronchopulmonary dysplasia (BPD) breathe using ventilators. Currently, doctors across the country and around the world use a variety of different approaches.  These variations depend on the severity of a patient’s medical condition, the ventilator settings used, and location-specific standards. Matthew Kielt, MD, a…

Beyond the Wow Factor: Artificial Intelligence in Pediatrics
 Beyond the Wow Factor: Artificial Intelligence in Pediatrics 1024 576 Katie Brind'Amour, PhD, MS, CHES
By: Katie Brind'Amour, PhD, MS, CHES

What promise do AI and machine learning hold for pediatrics, and how can their potential flourish while still safeguarding children’s health and privacy? Machine learning (ML) and artificial intelligence (AI) have exploded across the worlds of marketing and commerce in recent years. Streaming services track what you watch and suggest other content you may enjoy.…

Using Machine Learning in the Electronic Medical Record to Save Lives
 Using Machine Learning in the Electronic Medical Record to Save Lives 1024 683 Abbie Miller
By: Abbie Miller
physicians in white coats looking at a tablet

The deterioration risk index identifies patients at risk for deterioration and poor outcomes, triggering the care team to act before a crisis happens. In a report published in Pediatric Critical Care Medicine, a team from Nationwide Children’s Hospital describes a machine learning tool for timely identification of hospitalized children at risk for deterioration – a…