Usage of EpiFinder clinical decision support in the assessment of epilepsy

NLP-based tools for localization of the epileptogenic zone in patients with drug-resistant focal epilepsy

Epilepsy surgery is an option for people with focal onset drug-resistant (DR) seizures but a delayed or incorrect diagnosis of epileptogenic zone (EZ) location limits its efficacy. Seizure semiological manifestations and their chronological appearance contain valuable information on the putative EZ location but their interpretation relies on extensive experience. The aim of our work is to support the localization of EZ in DR patients automatically analyzing the semiological description of seizures contained in video-EEG reports. Our sample is composed of 536 descriptions of seizures extracted from Electronic Medical Records of 122 patients. We devised numerical representations of anamnestic records and seizures descriptions, exploiting Natural Language Processing (NLP) techniques, and used them to feed Machine Learning (ML) models. We performed three binary classification tasks: localizing the EZ in the right or left hemisphere, temporal or extra-temporal, and frontal or posterior regions. Our computational pipeline reached performances above 70% in all tasks. These results show that NLP-based numerical representation combined with ML-based classification models may help in localizing the origin of the seizures relying only on seizures-related semiological text data alone. Accurate early recognition of EZ could enable a more appropriate patient management and a faster access to epilepsy surgery to potential candidates.

Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine

Mounting evidence has highlighted the implementation of big data handling and management in the healthcare industry to improve the clinical services. Various private and public companies have generated, stored, and analyzed different types of big healthcare data, such as omics data, clinical data, electronic health records, personal health records, and sensing data with the aim to move in the direction of precision medicine. Additionally, with the advancement in technologies, researchers are curious to extract the potential involvement of artificial intelligence and machine learning on big healthcare data to enhance the quality of patient's lives. However, seeking solutions from big healthcare data requires proper management, storage, and analysis, which imposes hinderances associated with big data handling. Herein, we briefly discuss the implication of big data handling and the role of artificial intelligence in precision medicine. Further, we also highlighted the potential of artificial intelligence in integrating and analyzing the big data that offer personalized treatment. In addition, we briefly discuss the applications of artificial intelligence in personalized treatment, especially in neurological diseases. Lastly, we discuss the challenges and limitations imposed by artificial intelligence in big data management and analysis to hinder precision medicine.

Machine learning applications to differentiate comorbid functional seizures and epilepsy from pure functional seizures

PURPOSE

We have utilized different methods in machine learning (ML) to develop the best algorithm to differentiate comorbid functional seizures (FS) and epilepsy from those who have pure FS.

METHODS

This was a retrospective study of an electronic database of patients with seizures. All patients with a diagnosis of FS (with or without comorbid epilepsy) were studied at the outpatient epilepsy clinic at Shiraz University of Medical Sciences, Shiraz, Iran, from 2008 until 2021. We arbitrarily selected 14 features that are important in making the diagnosis of patients with seizures and also are easily obtainable during history taking. Pytorch and Scikit-learn packages were used to construct various models including random forest classifier, decision tree classifier, support vector classifier, k-nearest neighbor, and TabNet classifier.

RESULTS

Three hundred and two patients had FS (82.5%), while 64 patients had FS and comorbid epilepsy (17.5%). The "TabNet classifier" could provide the best sensitivity (90%) and specificity (74%) measures (accuracy of 76%) to help differentiate patients with FS from those with FS and comorbid epilepsy.

CONCLUSION

These satisfactory differentiating measures suggest that the current algorithm could be used in clinical practice to help with the difficult task of distinguishing patients with FS from those with FS and comorbid epilepsy. Based on the results of the current study, we have developed an Application (SeiDx). This App is freely accessible at the following address: https://drive.google.com/file/d/1rAgBXKNPW9bmUCDioaGHHzLBQgzZ-HZ2/view. This App should be validated in a prospective assessment.

A web-based algorithm to rapidly classify seizures for the purpose of drug selection

OBJECTIVE

To develop and validate a pragmatic algorithm that classifies seizure types, to facilitate therapeutic decision-making.

METHODS

Using a modified Delphi method, five experts developed a pragmatic classification of nine types of epileptic seizures or combinations of seizures that influence choice of medication, and constructed a simple algorithm, freely available on the internet. The algorithm consists of seven questions applicable to patients with seizure onset at the age of 10 years or older. Questions to screen for nonepileptic attacks were added. Junior physicians, nurses, and physician assistants applied the algorithm to consecutive patients in a multicenter prospective validation study (ClinicalTrials.gov identifier: NCT03796520). The reference standard was the seizure classification by expert epileptologists, based on all available data, including electroencephalogram (EEG), video-EEG monitoring, and neuroimaging. In addition, physicians working in underserved areas assessed the feasibility of using the web-based algorithm in their clinical setting.

RESULTS

A total of 262 patients were assessed, of whom 157 had focal, 51 had generalized, and 10 had unknown onset epileptic seizures, and 44 had nonepileptic paroxysmal events. Agreement between the algorithm and the expert classification was 83.2% (95% confidence interval = 78.6%-87.8%), with an agreement coefficient (AC1) of .82 (95% confidence interval = .77-.87), indicating almost perfect agreement. Thirty-two health care professionals from 14 countries evaluated the feasibility of the web-based algorithm in their clinical setting, and found it applicable and useful for their practice (median = 6.5 on 7-point Likert scale).

SIGNIFICANCE

The web-based algorithm provides an accurate classification of seizure types, which can be used for selecting antiseizure medications in adolescents and adults.

Can Big Data guide prognosis and clinical decisions in epilepsy?

Big Data is no longer a novel concept in health care. Its promise of positive impact is not only undiminished, but daily enhanced by seemingly endless possibilities. Epilepsy is a disorder with wide heterogeneity in both clinical and research domains, and thus lends itself to Big Data concepts and techniques. It is therefore inevitable that Big Data will enable multimodal research, integrating various aspects of "-omics" domains, such as phenome, genome, microbiome, metabolome, and proteome. This scope and granularity have the potential to change our understanding of prognosis and mortality in epilepsy. The scale of new discovery is unprecedented due to the possibilities promised by advances in machine learning, in particular deep learning. The subsequent possibilities of personalized patient care through clinical decision support systems that are evidence-based, adaptive, and iterative seem to be within reach. A major objective is not only to inform decision-making, but also to reduce uncertainty in outcomes. Although the adoption of electronic health record (EHR) systems is near universal in the United States, for example, advanced clinical decision support in or ancillary to EHRs remains sporadic. In this review, we discuss the role of Big Data in the development of clinical decision support systems for epilepsy care, prognostication, and discovery.

A Model for Examining Challenges and Opportunities in Use of Cloud Computing for Health Information Systems

Health Information Systems (HIS) are becoming crucial for health providers, not only for keeping Electronic Health Records (EHR) but also because of the features they provide that can be lifesaving, thanks to the advances in Information Technology (IT). These advancements have led to increasing demands for additional features to these systems to improve their intelligence, reliability, and availability. All these features may be provisioned through the use of cloud computing in HIS. This study arrives at three dimensions pertinent to adoption of cloud computing in HIS through extensive interviews with experts, professional expertise and knowledge of one of the authors working in this area, and review of academic and practitioner literature. These dimensions are financial performance and cost; IT operational excellence and DevOps; and security, governance, and compliance. Challenges and drivers in each of these dimensions are detailed and operationalized to arrive at a model for HIS adoption. This proposed model detailed in this study can be employed by executive management of health organizations, especially senior clinical management positions like Chief Technology Officers (CTOs), Chief Information Officers (CIOs), and IT managers to make an informed decision on adoption of cloud computing for HIS. Use of cloud computing to support operational and financial excellence of healthcare organizations has already made some headway in the industry, and its use in HIS would be a natural next step. However, due to the mission′s critical nature and sensitivity of information stored in HIS, the move may need to be evaluated in a holistic fashion that can be aided by the proposed dimensions and the model. The study also identifies some issues and directions for future research for cloud computing adoption in the context of HIS.

[Decision support systems, assistance systems and telemedicine in epileptology]

Hintergrund

Die wissenschaftlichen Erkenntnisse über Epilepsien und deren klinische Implikationen nehmen rasant zu. Für Nichtexperten stellt sich die zunehmende Herausforderung, den Überblick hierüber zu bewahren. Hier setzen Clinical-decision-support-Systeme (CDSS) an, indem sie standard- und expertengetriggertes Wissen zur Diagnostik und Therapie individualisiert und automatisiert liefern. Zudem sind Medizin-Apps und telemedizinische Verfahren zur Diagnostik und Therapie sowie Assistenzsysteme zur Anfallsdetektion bei Epilepsien verfügbar.

Ziel der Arbeit

Es soll ein Überblick über die aktuellen Entwicklungen und Anwendungsmöglichkeiten verfügbarer tele-epileptologischer Methoden gegeben werden.

Material und Methoden

Auf der Basis persönlicher Kenntnis und eines Literaturreviews werden epilepsiespezifische CDSS, Medizin-Apps, Assistenzsysteme sowie telemedizinische Anwendungen charakterisiert und deren klinische Einsatzmöglichkeiten dargestellt.

Ergebnisse und Diskussion

Personen mit Epilepsie könnten aufgrund des chronischen Verlaufs und der Komplexität der Erkrankung und ihrer Folgen von CDSS profitieren. Es erscheint wünschenswert, dass epilepsiespezifische CDSS sowohl für die Behandelnden als auch für Patienten nutzbar werden. Apps für Menschen mit Epilepsie dienen derzeit meist der Verlaufsdokumentation von Anfallsfrequenz, Medikamentencompliance und Nebenwirkungen. Gegenwärtige Anfallsdetektionssysteme erkennen vor allem generalisiert tonisch-klonische Anfälle (GTKA). Ein klinischer Nutzen ist noch nicht hinreichend belegt, erscheint aber wahrscheinlich, insbesondere da GTKA mit dem Risiko eines plötzlichen Todes von Epilepsiepatienten assoziiert sind und Interventionen als wirksam gelten.

Artificial intelligence as an emerging technology in the current care of neurological disorders

BACKGROUND

Artificial intelligence (AI) has influenced all aspects of human life and neurology is no exception to this growing trend. The aim of this paper is to guide medical practitioners on the relevant aspects of artificial intelligence, i.e., machine learning, and deep learning, to review the development of technological advancement equipped with AI, and to elucidate how machine learning can revolutionize the management of neurological diseases. This review focuses on unsupervised aspects of machine learning, and how these aspects could be applied to precision neurology to improve patient outcomes. We have mentioned various forms of available AI, prior research, outcomes, benefits and limitations of AI, effective accessibility and future of AI, keeping the current burden of neurological disorders in mind.

DISCUSSION

The smart device system to monitor tremors and to recognize its phenotypes for better outcomes of deep brain stimulation, applications evaluating fine motor functions, AI integrated electroencephalogram learning to diagnose epilepsy and psychological non-epileptic seizure, predict outcome of seizure surgeries, recognize patterns of autonomic instability to prevent sudden unexpected death in epilepsy (SUDEP), identify the pattern of complex algorithm in neuroimaging classifying cognitive impairment, differentiating and classifying concussion phenotypes, smartwatches monitoring atrial fibrillation to prevent strokes, and prediction of prognosis in dementia are unique examples of experimental utilizations of AI in the field of neurology. Though there are obvious limitations of AI, the general consensus among several nationwide studies is that this new technology has the ability to improve the prognosis of neurological disorders and as a result should become a staple in the medical community.

CONCLUSION

AI not only helps to analyze medical data in disease prevention, diagnosis, patient monitoring, and development of new protocols, but can also assist clinicians in dealing with voluminous data in a more accurate and efficient manner.