Epilepsy informatics and an ontology-driven infrastructure for large database research and patient care in epilepsy

Design of the formalized and integrated Alzheimer's Disease Ontology and its application in retrieving textual data via text mining

As one of the leading causes for dementia in the population, it is imperative that we discern exactly why Alzheimer's disease (AD) has a strong molecular association with beta-amyloid and tau. Although a clear understanding about etiology and pathogenesis of AD remains unsolved, scientists worldwide have dedicated significant efforts to discovering the molecular interactions linked to the pathological characteristics and potential treatments. Knowledge representations, such as domain ontologies encompassing our current understanding about AD, could greatly assist and contribute to disease research. This paper describes the construction and application of the integrated Alzheimer's Disease Ontology (ADO), combining selected concepts from the former version of the ADO and the Alzheimer's Disease Mapping Ontology (ADMO). In addition to the existing entities available from these knowledge models, essential knowledge about AD from public sources, such as newly discovered risk factor genes and novel treatments, was also integrated. The ADO can also be leveraged in text mining scenarios given that it is conceptually enriched with domain-specific knowledge as well as their relations. The integrated ADO consists of 39 855 total axioms. The ontology covers many aspects of the AD domain, including risk factor genes, clinical features, treatments and experimental models. The ontology complies with the Open Biological and Biomedical Ontology principles and was accepted by the foundry. In this paper, we illustrate the role of the presented ontology in extracting textual information from the SCAIView database and key measures in an ADO-based corpus. Database URL:  https://academic.oup.com/database.

Early prediction of encephalopathic transformation in children with benign epilepsy with centro-temporal spikes

BACKGROUND

Most children with Benign epilepsy with centro-temporal spikes (BECTS) undergo remission during late adolescence and do not require treatment. In a small group of patients, the condition may evolve to encephalopathic syndromes including epileptic encephalopathy with continuous spike-and-wave during sleep (ECSWS), or Landau-Kleffner Syndrome (LKS). Development of prediction models for early identification of at-risk children is of utmost importance.

AIM

To develop a predictive model of encephalopathic transformation using data-driven approaches, reveal complex interactions to identify potential risk factors.

METHODS

Data were collected from a cohort of 91 patients diagnosed with BECTS treated between the years 2005-2017 at a pediatric neurology institute. Data on the initial presentation was collected based on a novel BECTS ontology and used to discover potential risk factors and to build a predictive model. Statistical and machine learning methods were compared.

RESULTS

A subgroup of 18 children had encephalopathic transformation. The least absolute shrinkage and selection operator (LASSO) regression Model with Elastic Net was able to successfully detect children with ECSWS or LKS. Sensitivity and specificity were 0.83 and 0.44. The most notable risk factors were fronto-temporal and temporo-parietal localization of epileptic foci, semiology of seizure involving dysarthria or somatosensory auras.

CONCLUSION

Novel prediction model for early identification of patients with BECTS at risk for ECSWS or LKS. This model can be used as a screening tool and assist physicians to consider special management for children predicted at high-risk. Clinical application of machine learning methods opens new frontiers of personalized patient care and treatment.

A propositional AI system for supporting epilepsy diagnosis based on the 2017 epilepsy classification: Illustrated by Dravet syndrome

PURPOSE

The 2017 epilepsy and seizure diagnosis framework emphasizes epilepsy syndromes and the etiology-based approach. We developed a propositional artificial intelligence (AI) system based on the above concepts to support physicians in the diagnosis of epilepsy.

METHODS

We analyzed and built ontology knowledge for the classification of seizure patterns, epilepsy, epilepsy syndrome, and etiologies. Protégé ontology tool was applied in this study. In order to enable the system to be close to the inferential thinking of clinical experts, we classified and constructed knowledge of other epilepsy-related knowledge, including comorbidities, epilepsy imitators, epilepsy descriptors, characteristic electroencephalography (EEG) findings, treatments, etc. We used the Ontology Web Language with Description Logic (OWL-DL) and Semantic Web Rule Language (SWRL) to design rules for expressing the relationship between these ontologies.

RESULTS

Dravet syndrome was taken as an illustration for epilepsy syndromes implementation. We designed an interface for the physician to enter the various characteristics of the patients. Clinical data of an 18-year-old boy with epilepsy was applied to the AI system. Through SWRL and reasoning engine Drool's execution, we successfully demonstrate the process of differential diagnosis.

CONCLUSION

We developed a propositional AI system by using the OWL-DL/SWRL approach to deal with the complexity of current epilepsy diagnosis. The experience of this system, centered on the clinical epilepsy syndromes, paves a path to construct an AI system for further complicated epilepsy diagnosis.

Big data in status epilepticus

Status epilepticus care and treatment are already being touched by the revolution in data science. New approaches designed to leverage the tremendous potential of "big data" in the clinical sphere are enabling researchers and clinicians to extract information from sources such as administrative claims data, the electronic medical health record, and continuous physiologic monitoring data streams. Algorithmic methods of data extraction also offer potential to fuse multimodal data (including text-based documentation, imaging data, and time-series data) to improve patient assessment and stratification beyond the manual capabilities of individual physicians. Still, the potential of data science to impact the diagnosis, treatment, and minute-to-minute care of patients with status epilepticus is only starting to be appreciated. In this brief review, we discuss how data science is impacting the field and draw examples from the following three main areas: (1) analysis of insurance claims from large administrative datasets to evaluate the impact of continuous electroencephalogram (EEG) monitoring on clinical outcomes; (2) natural language processing of the electronic health record to find, classify, and stratify patients for prognostication and treatment; and (3) real-time systems for data analysis, data reduction, and multimodal data fusion to guide therapy in real time. While early, it is our hope that these examples will stimulate investigators to leverage data science, computer science, and engineering methods to improve the care and outcome of patients with status epilepticus and other neurological disorders. This article is part of the Special Issue "Proceedings of the 7th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures".

Anticonvulsant effect of piperine ameliorates memory impairment, inflammation and oxidative stress in a rat model of pilocarpine-induced epilepsy

The primary active component of black pepper is piperine, which is purified and used to treat epilepsy, achieving higher efficiency when purified. The present study was conducted to evaluate whether the anticonvulsant effect of piperine ameliorates pilocarpine-induced epilepsy, and to investigate the mechanism underlying these effects. Epilepsy was induced in Sprague Dawley rats using pilocarpine. Pilocarpine-induced epilepsy in the rats was treated with 40 mg/kg piperine for 45 consecutive days. Status epilepticus and a Morris water maze test were used to analyze the anticonvulsant effects of piperine in the epileptic rats. Inflammation and oxidative stress were then measured using commercially-available kits following piperine treatment. Lastly, the activity of caspase-3 and the protein expression levels of B-cell lymphoma 2 (Bcl-2) and Bcl-2-associated X protein (Bax) were evaluated using commercially-available kits and western blot analysis, respectively. The results demonstrated that treatment with piperine was able to reduce the status epilepticus and prevented memory impairment following pilocarpine-induced epilepsy in rats. The anticonvulsant effects of piperine decreased inflammation and oxidative stress following pilocarpine-induced epilepsy in rats. The upregulated activity of caspase-3 and expression levels of Bax/Bcl-2 were suppressed following treatment with piperine in the rats with pilocarpine-induced epilepsy. These results suggest that the anticonvulsant effects of piperine ameliorate memory impairment, inflammation and oxidative stress in a rat model of pilocarpine-induced epilepsy.

Managing information well: Toward an ontology-driven informatics platform for data sharing and secondary use in epilepsy self-management research centers

Epilepsy is a chronic neurological condition that requires active self-management to reduce personal and population burden. The Managing Epilepsy Well Network, funded by the US Centers for Disease Control and Prevention, conducts research on epilepsy self-management. There is an urgent need to develop an integrated informatics platform to maximize the secondary use of existing Managing Epilepsy Well Network data. We have implemented multiple steps to develop an informatics platform, including: (a) a survey of existing outcome data, (b) identification of common data elements, and (c) an integrated database using an epilepsy domain ontology to reconcile data heterogeneity. The informatics platform enables assessment of epilepsy self-management samples by site and in aggregate to support data interpretations for clinical care and ongoing epilepsy self-management research. The Managing Epilepsy Well informatics platform is expected to help advance epilepsy self-management, improve health outcomes, and has potential application in other thematic research networks.

MEDCIS: Multi-Modality Epilepsy Data Capture and Integration System

Sudden Unexpected Death in Epilepsy (SUDEP) is the leading mode of epilepsy-related death and is most common in patients with intractable, frequent, and continuing seizures. A statistically significant cohort of patients for SUDEP study requires meticulous, prospective follow up of a large population that is at an elevated risk, best represented by the Epilepsy Monitoring Unit (EMU) patient population. Multiple EMUs need to collaborate, share data for building a larger cohort of potential SUDEP patient using a state-of-the-art informatics infrastructure. To address the challenges of data integration and data access from multiple EMUs, we developed the Multi-Modality Epilepsy Data Capture and Integration System (MEDCIS) that combines retrospective clinical free text processing using NLP, prospective structured data capture using an ontology-driven interface, interfaces for cohort search and signal visualization, all in a single integrated environment. A dedicated Epilepsy and Seizure Ontology (EpSO) has been used to streamline the user interfaces, enhance its usability, and enable mappings across distributed databases so that federated queries can be executed. MEDCIS contained 936 patient data sets from the EMUs of University Hospitals Case Medical Center (UH CMC) in Cleveland and Northwestern Memorial Hospital (NMH) in Chicago. Patients from UH CMC and NMH were stored in different databases and then federated through MEDCIS using EpSO and our mapping module. More than 77GB of multi-modal signal data were processed using the Cloudwave pipeline and made available for rendering through the web-interface. About 74% of the 40 open clinical questions of interest were answerable accurately using the EpSO-driven VISual AGregagator and Explorer (VISAGE) interface. Questions not directly answerable were either due to their inherent computational complexity, the unavailability of primary information, or the scope of concept that has been formulated in the existing EpSO terminology system.

The neurological disease ontology

BACKGROUND

We are developing the Neurological Disease Ontology (ND) to provide a framework to enable representation of aspects of neurological diseases that are relevant to their treatment and study. ND is a representational tool that addresses the need for unambiguous annotation, storage, and retrieval of data associated with the treatment and study of neurological diseases. ND is being developed in compliance with the Open Biomedical Ontology Foundry principles and builds upon the paradigm established by the Ontology for General Medical Science (OGMS) for the representation of entities in the domain of disease and medical practice. Initial applications of ND will include the annotation and analysis of large data sets and patient records for Alzheimer's disease, multiple sclerosis, and stroke.

DESCRIPTION

ND is implemented in OWL 2 and currently has more than 450 terms that refer to and describe various aspects of neurological diseases. ND directly imports the development version of OGMS, which uses BFO 2. Term development in ND has primarily extended the OGMS terms 'disease', 'diagnosis', 'disease course', and 'disorder'. We have imported and utilize over 700 classes from related ontology efforts including the Foundational Model of Anatomy, Ontology for Biomedical Investigations, and Protein Ontology. ND terms are annotated with ontology metadata such as a label (term name), term editors, textual definition, definition source, curation status, and alternative terms (synonyms). Many terms have logical definitions in addition to these annotations. Current development has focused on the establishment of the upper-level structure of the ND hierarchy, as well as on the representation of Alzheimer's disease, multiple sclerosis, and stroke. The ontology is available as a version-controlled file at http://code.google.com/p/neurological-disease-ontology along with a discussion list and an issue tracker.

CONCLUSION

ND seeks to provide a formal foundation for the representation of clinical and research data pertaining to neurological diseases. ND will enable its users to connect data in a robust way with related data that is annotated using other terminologies and ontologies in the biomedical domain.