Revisiting the Endoscopic Third Ventriculostomy Success Score using machine learning: can we do better?

Objective: The Endoscopic Third Ventriculostomy Success Score (ETVSS) is a useful decision-making heuristic when considering the probability of surgical success, defined traditionally as no repeat cerebrospinal fluid diversion surgery needed within 6 months. Nonetheless, the performance of the logistic regression (LR) model in the original 2009 study was modest, with an area under the receiver operating characteristic curve (AUROC) of 0.68.

Machine learning in the diagnosis, management, and care of patients with low back pain: a scoping review of the literature and future directions.

Background Context: Low back pain (LBP) remains the leading cause of disability globally. In recent years, machine learning (ML) has emerged as a potentially useful tool to aid the diagnosis, management, and prognostication of LBP.

Purpose: In this review, we assess the scope of ML applications in the LBP literature and outline gaps and opportunities.

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.

Predicting seizure recurrence after an initial seizure-like episode from routine clinical notes using large language models: a retrospective cohort study.

Background: The evaluation and management of first-time seizure-like events in children can be difficult because these episodes are not always directly observed and might be epileptic seizures or other conditions (seizure mimics). We aimed to evaluate whether machine learning models using real-world data could predict seizure recurrence after an initial seizure-like event.

Methods: This retrospective cohort study compared models trained and evaluated on two separate datasets between Jan 1, 2010, and Jan 1, 2020: electronic medical records (EMRs) at Boston Children's Hospital and de-identified, patient-level, administrative claims data from the IBM MarketScan research database.

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.

Phenotype-Agnostic Molecular Subtyping of Neurodegenerative Disorders: The Cincinnati Cohort Biomarker Program (CCBP).

Ongoing biomarker development programs have been designed to identify serologic or imaging signatures of clinico-pathologic entities, assuming distinct biological boundaries between them. Identified putative biomarkers have exhibited large variability and inconsistency between cohorts, and remain inadequate for selecting suitable recipients for potential disease-modifying interventions. We launched the Cincinnati Cohort Biomarker Program (CCBP) as a population-based, phenotype-agnostic longitudinal study.

An interactive online dashboard for tracking COVID-19 in U.S. counties, cities, and states in real time.

Objective: The study sought to create an online resource that informs the public of coronavirus disease 2019 (COVID-19) outbreaks in their area.

Materials And Methods: This R Shiny application aggregates data from multiple resources that track COVID-19 and visualizes them through an interactive, online dashboard.

Results: The Web resource, called the COVID-19 Watcher, can be accessed online (https://covid19watcher.

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).

Functional neurological disorders in Parkinson disease.

Objective: To ascertain demographic and clinical features of Parkinson disease (PD) associated with functional neurological features.

Methods: A standardised form was used to extract data from electronic records of 53 PD patients with associated functional neurological disorders (PD-FND) across eight movement disorders centres in the USA, Canada and Europe. These subjects were matched for age, gender and disease duration to PD patients without functional features (PD-only).

Tablet-Based Application for Objective Measurement of Motor Fluctuations in Parkinson Disease.

Background: The motor subscale of the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS-III) has limited applicability for the assessment of motor fluctuations in the home setting.

Methods: To assess whether a self-administered, tablet-based application can reliably quantify differences in motor performance using two-target finger tapping and forearm pronation-supination tasks in the ON (maximal dopaminergic medication efficacy) and OFF (reemergence of parkinsonian deficits) medication states, we recruited 11 Parkinson disease (PD) patients (age, 60.6 ± 9.

Biometric Digital Health Technology for Measuring Motor Function in Parkinson's Disease: Results from a Feasibility and Patient Satisfaction Study.

Objectives: To assess the feasibility, predictive value, and user satisfaction of objectively quantifying motor function in Parkinson's disease (PD) through a tablet-based application (iMotor) using self-administered tests.

Methods: PD and healthy controls (HCs) performed finger tapping, hand pronation-supination and reaction time tasks using the iMotor application.

Results: Thirty-eight participants (19 with PD and 17 HCs) were recruited in the study.

Orthostatic hypotension in Parkinson's disease: Does it matter if asymptomatic?

Introduction: Orthostatic hypotension (OH) may frequently be asymptomatic in patients with Parkinson's disease (PD). However, the relationship between symptomatic/asymptomatic status and functional disability remains unclear.

Methods: Using orthostatic blood pressure (BP) measurements and the Orthostatic Hypotension Symptom Assessment (OHSA) questionnaire, 121 consecutive PD patients without history of chronic hypertension and not taking alpha-adrenergic antagonists for bladder disorders were classified according to (1) OH symptomatic status, based on presence/absence of orthostatic symptoms (symptomatic OH: OHSA item 1 ≥ 1), and (2) OH severity, based on the magnitude of BP fall on the lying-to-standing test: OH- (<20/10 mmHg); moderate OH+ (≥20/10 mmHg but < 30/15 mmHg); and severe OH+ (≥30/15 mmHg).

Which patients with epilepsy are at risk for psychogenic nonepileptic seizures (PNES)? A multicenter case-control study.

Objective: We sought to examine the clinical and electrographic differences between patients with combined epileptic (ES) and psychogenic nonepileptic seizures (PNES) and age- and gender-matched patients with ES-only and PNES-only.

Methods: Data from 138 patients (105 women [77%]), including 46 with PNES/ES (39±12years), 46 with PNES-only (39±11years), and 46 with ES-only (39±11years), were compared using logistic regression analysis after adjusting for clustering effect.

Results: In the cohort with PNES/ES, ES antedated PNES in 28 patients (70%) and occurred simultaneously in 11 (27.