A computational clinical decision-supporting system to suggest effective anti-epileptic drugs for pediatric epilepsy patients based on deep learning models using patient's medical history.

BACKGROUND: Epilepsy, a chronic brain disorder characterized by abnormal brain activity that causes seizures and other symptoms, is typically treated using anti-epileptic drugs (AEDs) as the first-line therapy. However, due to the variations in their modes of action, identification of effective AEDs often relies on ad hoc trials, which is particularly challenging for pediatric patients. Thus, there is significant value in computational methods capable of assisting in the selection of AEDs, aiming to minimize unnecessary medication and improve treatment efficacy. RESULTS: In this study, we collected 7,507 medical records from 1,000 pediatric epilepsy patients and developed a computational clinical decision-supporting system for AED selection. This system leverages three multi-channel convolutional neural network (CNN) models tailored to three specific AEDs (vigabatrin, prednisolone, and clobazam). Each CNN model predicts whether a respective AED is effective on a given patient or not. The CNN models showed AUROCs of 0.90, 0.80, and 0.92 in 10-fold cross-validation, respectively. Evaluation on a hold-out test dataset further revealed positive predictive values (PPVs) of 0.92, 0.97, and 0.91 for the three respective CNN models, representing that suggested AEDs by our models would be effective in controlling epilepsy with a high accuracy and thereby reducing unnecessary medications for pediatric patients. CONCLUSION: Our CNN models in the system demonstrated high PPVs for the three AEDs, which signifies the potential of our approach to support the clinical decision-making by assisting doctors in recommending effective AEDs within the three AEDs for patients based on their medical history. This would result in a reduction in the number of unnecessary ad hoc attempts to find an effective AED for pediatric epilepsy patients.

Electroencephalography characteristics related to risk of sudden unexpected death in epilepsy in patients with Dravet syndrome.

Objective: To investigate the quantitative electroencephalography (EEG) features associated with a high risk of sudden unexpected death in epilepsy (SUDEP) in patients with Dravet syndrome (DS). Methods: Patients with DS and healthy controls (HCs) who underwent EEG were included in the study. EEG signals were recorded using a 21 channel digital EEG system, and pre-processed data were analyzed to identify quantitative EEG features associated with a high SUDEP risk. To assess the risk of SUDEP, SUDEP-7 scores were used. Results: A total of 64 patients with DS [38 males and 26 females, aged: 128.51 ± 75.50 months (range: 23-380 months)], and 13 HCs [7 males and 6 females, aged: 95.46 ± 86.48 months (range: 13-263 months)] were included. For the absolute band power, the theta power was significantly higher in the high-SUDEP group than in the low-SUDEP group in the central brain region. For the relative band power, the theta power was also significantly higher in the high-SUDEP group than in the low-SUDEP group in the central and occipital brain regions. The alpha power was significantly lower in the high-SUDEP group than in the low-SUDEP group in the central and parietal brain regions. Conclusion: Patients with high SUDEP-7 scores have different EEG features from those with low SUDEP-7 scores, suggesting that EEG may be used as a biomarker of SUDEP in DS. Significance: Early intervention in patients with DS at a high risk of SUDEP can reduce mortality and morbidity. Patients with high theta band powers warrant high-level supervision.