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Anticonvulsants

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How accurate are machine learning models in predicting anti-seizure medication responses: A systematic review.

Epilepsy & behavior : E&B
IMPORTANCE: Current epilepsy management protocols often depend on anti-seizure medication (ASM) trials and assessment of clinical response. This may delay the initiation of the ASM regimen that might optimally balance efficacy and tolerability for in...

Prediction of Pharmacoresistance in Drug-Naïve Temporal Lobe Epilepsy Using Ictal EEGs Based on Convolutional Neural Network.

Neuroscience bulletin
Approximately 30%-40% of epilepsy patients do not respond well to adequate anti-seizure medications (ASMs), a condition known as pharmacoresistant epilepsy. The management of pharmacoresistant epilepsy remains an intractable issue in the clinic. Its ...

Development and applications of a machine learning model for an in-depth analysis of pentylenetetrazol-induced seizure-like behaviors in adult zebrafish.

Neuroscience
Epilepsy, a neurological disorder causing recurring seizures, is often studied in zebrafish by exposing animals to pentylenetetrazol (PTZ), which induces clonic- and tonic-like behaviors. While adult zebrafish seizure-like behaviors are well characte...

Using artificial intelligence to optimize anti-seizure treatment and EEG-guided decisions in severe brain injury.

Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics
Electroencephalography (EEG) is invaluable in the management of acute neurological emergencies. Characteristic EEG changes have been identified in diverse neurologic conditions including stroke, trauma, and anoxia, and the increased utilization of co...

Prediction Trough Concentrations of Valproic Acid Among Chinese Adult Patients with Epilepsy Using Machine Learning Techniques.

Pharmaceutical research
OBJECTIVE: This study aimed to establish an optimal model based on machine learning (ML) to predict Valproic acid (VPA) trough concentrations in Chinese adult epilepsy patients.

Analysis of User-Generated Posts on Social Media of Adjuvant Analgesics: A Machine Learning Study.

International journal of medical sciences
Antiepileptics and antidepressants are frequently prescribed for chronic pain, but their efficacy and potential adverse effects raise concerns, including dependency issues. Increased prescriptions, sometimes fraudulent, prompted reclassification of ...

ML-based Models as a Strategy to Discover Novel Antiepileptic Drugs Targeting Sodium Receptor Channel.

Current topics in medicinal chemistry
BACKGROUND: Epilepsy remains the most common and chronic disorder demanding longterm management. The impact of epilepsy disease is a cause of great concern and has resulted in efforts to develop treatment for epilepsy. It occurs due to an increase in...

Inductive reasoning with large language models: A simulated randomized controlled trial for epilepsy.

Epilepsy research
INTRODUCTION: To investigate the potential of using artificial intelligence (AI), specifically large language models (LLMs), for synthesizing information in a simulated randomized clinical trial (RCT) for an anti-seizure medication, cenobamate, demon...

Classification and regression machine learning models for predicting mixed toxicity of carbamazepine and its transformation products.

Environmental research
Carbamazepine (CBZ) and its transformation products (TPs) often occur in aquatic environments in the form of mixtures, posing potential risks to ecosystems. However, establishing standardized protocols for synthesizing, isolating, and acquiring these...

Machine learning enables high-throughput, low-replicate screening for novel anti-seizure targets and compounds using combined movement and calcium fluorescence in larval zebrafish.

European journal of pharmacology
Identifying new anti-seizure medications (ASMs) is difficult due to limitations in animal-based assays. Zebrafish (Danio rerio) serve as a model for chemical and genetic seizures, but current methods for detecting anti-seizure responses are limited b...