An Explainable Multimodal Neural Network Architecture for Predicting Epilepsy Comorbidities Based on Administrative Claims Data.

Epilepsy is a complex brain disorder characterized by repetitive seizure events. Epilepsy patients often suffer from various and severe physical and psychological comorbidities (e.g.

Assessment and effect of a gap between new-onset epilepsy diagnosis and treatment in the US.

Objective: To estimate the treatment gap between a new epilepsy diagnosis and antiepileptic drug (AED) initiation in the United States.

Methods: Retrospective claims-based cohort study using Truven Health MarketScan databases (commercial and supplemental Medicare, calendar years 2010-2015; Medicaid, 2010-2014) and a validation study using PharMetrics Plus Database linked to LRx claims database (2009-2014). Persons met epilepsy diagnostic criteria, had an index date (first epilepsy diagnosis) with a preceding 2-year baseline (1 year for persons aged 1 to <2 years; none for persons <1 year), and continuous medical and pharmacy enrollment without epilepsy/seizure diagnosis or AED prescription during baseline.