Early Identification of Candidates for Epilepsy Surgery: A Multicenter, Machine Learning, Prospective Validation Study.

Background And Objectives: Epilepsy surgery is often delayed. We previously developed machine learning (ML) models to identify candidates for resective epilepsy surgery earlier in the disease course. In this study, we report the prospective validation.

Developmental Epidemiology of Pediatric Anxiety Disorders.

This review summarizes the developmental epidemiology of childhood and adolescent anxiety disorders. It discusses the coronavirus disease of 2019 (COVID-19) pandemic, sex differences, longitudinal course, and stability of anxiety disorders in addition to recurrence and remission. The trajectory of anxiety disorders-whether homotypic (ie, the same anxiety disorder persists over time) or heterotypic (ie, an anxiety disorder shifts to a different diagnosis over time) is discussed with regard to social, generalized, and separation anxiety disorders as well as specific phobia, and panic disorder.

Automated, machine learning-based alerts increase epilepsy surgery referrals: A randomized controlled trial.

Objective: To determine whether automated, electronic alerts increased referrals for epilepsy surgery.

Methods: We conducted a prospective, randomized controlled trial of a natural language processing-based clinical decision support system embedded in the electronic health record (EHR) at 14 pediatric neurology outpatient clinic sites. Children with epilepsy and at least two prior neurology visits were screened by the system prior to their scheduled visit.

Early identification of epilepsy surgery candidates: A multicenter, machine learning study.

Objectives: Epilepsy surgery is underutilized. Automating the identification of potential surgical candidates may facilitate earlier intervention. Our objective was to develop site-specific machine learning (ML) algorithms to identify candidates before they undergo surgery.

Cystic Fibrosis Point of Personalized Detection (CFPOPD): An Interactive Web Application.

Background: Despite steady gains in life expectancy, individuals with cystic fibrosis (CF) lung disease still experience rapid pulmonary decline throughout their clinical course, which can ultimately end in respiratory failure. Point-of-care tools for accurate and timely information regarding the risk of rapid decline is essential for clinical decision support.

Objective: This study aims to translate a novel algorithm for earlier, more accurate prediction of rapid lung function decline in patients with CF into an interactive web-based application that can be integrated within electronic health record systems, via collaborative development with clinicians.

Dynamic predictive probabilities to monitor rapid cystic fibrosis disease progression.

Cystic fibrosis (CF) is a progressive, genetic disease characterized by frequent, prolonged drops in lung function. Accurately predicting rapid underlying lung-function decline is essential for clinical decision support and timely intervention. Determining whether an individual is experiencing a period of rapid decline is complicated due to its heterogeneous timing and extent, and error component of the measured lung function.

Investigation of bias in an epilepsy machine learning algorithm trained on physician notes.

Racial disparities in the utilization of epilepsy surgery are well documented, but it is unknown whether a natural language processing (NLP) algorithm trained on physician notes would produce biased recommendations for epilepsy presurgical evaluations. To assess this, an NLP algorithm was trained to identify potential surgical candidates using 1097 notes from 175 epilepsy patients with a history of resective epilepsy surgery and 268 patients who achieved seizure freedom without surgery (total N = 443 patients). The model was tested on 8340 notes from 3776 patients with epilepsy whose surgical candidacy status was unknown (2029 male, 1747 female, median age = 9 years; age range = 0-60 years).

Gaps and opportunities in refractory status epilepticus research in children: a multi-center approach by the Pediatric Status Epilepticus Research Group (pSERG).

Purpose: Status epilepticus (SE) is a life-threatening condition that can be refractory to initial treatment. Randomized controlled studies to guide treatment choices, especially beyond first-line drugs, are not available. This report summarizes the evidence that guides the management of refractory convulsive SE (RCSE) in children, defines gaps in our clinical knowledge and describes the development and works of the 'pediatric Status Epilepticus Research Group' (pSERG).

Putting the pieces together: preliminary efficacy of a web-based family intervention for children with traumatic brain injury.

Objective: To report preliminary efficacy data from a Web-based family problem-solving intervention to improve parent and child adaptation.

Method: Eight parents and six children with moderate to severe traumatic brain injury (TBI) who were injured more than 15 months earlier (M = 16 months) participated in the intervention. Families were given computers, Web cameras, and high-speed Internet access.

A web-based family problem-solving intervention for families of children with traumatic brain injury.

We developed a Web-based intervention for pediatric traumatic brain injury (TBI) and examined its feasibility for participants with limited computer experience. Six families, including parents, siblings, and children with TBI, were given computers, Web cameras, and high-speed Internet access. Weekly videoconferences with the therapist were conducted after participants completed on-line interactive experiences on problem solving, communication, and TBI-specific behavior management.

Treatment of otitis media with observation and a safety-net antibiotic prescription.

Objective: Several studies have demonstrated that acute otitis media (AOM) in children can be managed without antibiotics. Because children with AOM have traditionally been treated with antibiotics in the United States, there are concerns that parents may not be comfortable with their children being treated with pain control alone. Recently, Cates in England showed that antibiotic usage for AOM could be decreased by prescribing a safety-net antibiotic prescription (SNAP) to be filled if symptoms do not resolve with observation after 48 hours.

Detection and visualization of compositionally similar cis-regulatory element clusters in orthologous and coordinately controlled genes.

Evolutionarily conserved noncoding genomic sequences represent a potentially rich source for the discovery of gene regulatory regions. However, detecting and visualizing compositionally similar cis-element clusters in the context of conserved sequences is challenging. We have explored potential solutions and developed an algorithm and visualization method that combines the results of conserved sequence analyses (BLASTZ) with those of transcription factor binding site analyses (MatInspector) (http://trafac.