Background: Identifying patients with intractable epilepsy who would benefit from therapeutic chronic vagal nerve stimulation (VNS) preoperatively remains a major clinical challenge. We have developed a statistical model for predicting VNS efficacy using only routine preimplantation electroencephalogram (EEG) recorded with the TruScan EEG device (Brazdil et al., 2019). It remains to be seen, however, if this model can be applied in different clinical settings.Objective: To validate our model using EEG data acquired with a different recording system.Methods: We identified a validation cohort of eight patients implanted with VNS, whose preimplantation EEG was recorded on the BrainScope device and who underwent the EEG recording according to the protocol. The classifier developed in our earlier work, named Pre-X-Stim, was then employed to classify these patients as predicted responders or non-responders based on the dynamics in EEG power spectra. Predicted and real-world outcomes were compared to establish the applicability of this classifier. In total, two validation experiments were performed using two different validation approaches (single classifier or classifier voting).Results: The classifier achieved 75% accuracy, 67% sensitivity, and 100% specificity. Only two patients, both real-life responders, were classified incorrectly in both validation experiments.Conclusion: We have validated the Pre-X-Stim model on EEGs from a different recording system, which indicates its application under different technical conditions. Our approach, based on preoperative EEG, is easily applied and financially undemanding and presents great potential for real-world clinical use.
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Online registers contain a large amount of data about healthcare providers in the Czech Republic. Information is available to all citizens and can be useful to patients, governmental organisations or employers. Based on these data, we are able to create a high-quality snapshot of the current state of healthcare providers. Interconnecting data from more data sources together is an interesting task, and accomplishing it enables us to ask more complex questions. This paper focuses on answering several questions about dentists in our country. A dataset from one online database was created, using automated data mining methods and a subsequent analysis. Results are presented via an online tool, which was provided to owners of the data. They reviewed our results and decided to use our findings for the presentation to the Czech government and subsequent negotiation processes. Our paper describes used methods, shows some results and outlines possibilities for further work.
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