AIMC Topic: Anticonvulsants
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Can we predict anti-seizure medication response in focal epilepsy using machine learning?
Clinical neurology and neurosurgery
Nov 16, 2021
OBJECTIVE: The aim of this study was to evaluate the feasibility of machine learning approach based on clinical factors and diffusion tensor imaging (DTI) to predict anti-seizure medication (ASM) response in focal epilepsy. We hypothesized that ASM r...
Graph-theory based degree centrality combined with machine learning algorithms can predict response to treatment with antiepileptic medications in children with epilepsy.
Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Jul 26, 2021
BACKGROUND AND PURPOSE: The purpose of the current study is to detect changes of graph-theory-based degree centrality (DC) and their relationship with the clinical treatment effects of anti-epileptic drugs (AEDs) for patients with childhood absence e...
Identification of focal epilepsy by diffusion tensor imaging using machine learning.
Acta neurologica Scandinavica
Mar 18, 2021
OBJECTIVE: The aim of this study was to evaluate the feasibility of machine learning based on diffusion tensor imaging (DTI) measures to distinguish patients with focal epilepsy versus healthy controls and antiseizure medication (ASM) responsiveness.
Deep learning and feature based medication classifications from EEG in a large clinical data set.
Scientific reports
Aug 26, 2020
The amount of freely available human phenotypic data is increasing daily, and yet little is known about the types of inferences or identifying characteristics that could reasonably be drawn from that data using new statistical methods. One data type ...
Prediction of antiepileptic drug treatment outcomes of patients with newly diagnosed epilepsy by machine learning.
Epilepsy & behavior : E&B
May 20, 2019
OBJECTIVE: The objective of this study was to build a supervised machine learning-based classifier, which can accurately predict the outcomes of antiepileptic drug (AED) treatment of patients with newly diagnosed epilepsy.
Predicting drug-resistant epilepsy - A machine learning approach based on administrative claims data.
Epilepsy & behavior : E&B
Nov 7, 2018
Patients with drug-resistant epilepsy (DRE) are at high risk of morbidity and mortality, yet their referral to specialist care is frequently delayed. The ability to identify patients at high risk of DRE at the time of treatment initiation, and to sub...
Therapeutic drug monitoring of levetiracetam in daily clinical practice: high-performance liquid chromatography versus immunoassay.
European journal of hospital pharmacy : science and practice
Oct 20, 2018
OBJECTIVES: Although levetiracetam presents an easy dosing and tolerability, therapeutic drug monitoring may be recommended in certain situations. Measurement of levetiracetam in serum plasma is commonly done by high performance liquid chromatography...
Personalized prediction model for seizure-free epilepsy with levetiracetam therapy: a retrospective data analysis using support vector machine.
British journal of clinical pharmacology
Sep 3, 2018
AIMS: To predict the probability of a seizure-free (SF) state in patients with epilepsy (PWEs) after treatment with levetiracetam and to identify the clinical and electroencephalographic (EEG) factors that affect outcomes.
ML-based Models as a Strategy to Discover Novel Antiepileptic Drugs Targeting Sodium Receptor Channel.
Current topics in medicinal chemistry
Jan 1, 2025
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...
Predicting Antiseizure Medication Treatment in Children with Rare Tuberous Sclerosis Complex-Related Epilepsy Using Deep Learning.
AJNR. American journal of neuroradiology
Dec 1, 2023
BACKGROUND AND PURPOSE: Tuberous sclerosis complex disease is a rare, multisystem genetic disease, but appropriate drug treatment allows many pediatric patients to have positive outcomes. The purpose of this study was to predict the effectiveness of ...
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