Machine learning model to predict the efficacy of antiseizure medications in patients with familial genetic generalized epilepsy

Wu, Junhong; Wu, Yan; Xiang, Ling; Gu, Yixue; Yin, Yan; Li, Lulin; Tian, Xin; Wei, Jing; Wang, Xuefeng

doi:10.1016/j.eplepsyres.2022.106888

Cited by 6 publications

(4 citation statements)

References 37 publications

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“…Recently, Zhao et al [28] have shown that a multilayer perceptron model was able to predict drug resistance in pediatric patients with epilepsy and tuberous sclerosis (AUC 0.812). Wu et al [29] have similarly shown that a random forest classifier can provide prediction of response to ASMs in the context of familial genetic generalized epilepsy (accuracy 91.2%). These recent studies have been welcome additions to the existing literature that showed that ML models can be a useful tool in the early prediction of drug resistance [30,31].…”

Section: Machine Learning Applications To Guide Medical Therapy and P...mentioning

confidence: 99%

Predictive models of epilepsy outcomes

Sheikh,

Jehi

2024

Current Opinion in Neurology

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Purpose of review Multiple complex medical decisions are necessary in the course of a chronic disease like epilepsy. Predictive tools to assist physicians and patients in navigating this complexity have emerged as a necessity and are summarized in this review. Recent findings Nomograms and online risk calculators are user-friendly and offer individualized predictions for outcomes ranging from safety of antiseizure medication withdrawal (accuracy 65–73%) to seizure-freedom, naming, mood, and language outcomes of resective epilepsy surgery (accuracy 72–81%). Improving their predictive performance is limited by the nomograms’ inability to ingest complex data inputs. Conversely, machine learning offers the potential of multimodal and expansive model inputs achieving human-expert level accuracy in automated scalp electroencephalogram (EEG) interpretation but lagging in predictive performance or requiring validation for other applications. Summary Good to excellent predictive models are now available to guide medical and surgical epilepsy decision-making with nomograms offering individualized predictions and user-friendly tools, and machine learning approaches offering the potential of improved performance. Future research is necessary to bridge the two approaches for optimal translation to clinical care.

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Section: Machine Learning Applications To Guide Medical Therapy and P...mentioning

confidence: 99%

Predictive models of epilepsy outcomes

Sheikh,

Jehi

2024

Current Opinion in Neurology

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“…Many recent studies have developed ML models for identifying the best choice of ASMs for patients with epilepsy. [73][74][75][76][77] In one study, the use of ML predicted ASM regimens associated with improved outcomes and reduced costs due to lower healthcare utilization rates. 74 A recent study, using data from nearly 1800 adults with newly diagnosed epilepsy across four countries, developed a deep ML model 77 to predict the effectiveness of an ASM, defined as at least one year of seizure freedom.…”

Section: Future Of Asm Therapymentioning

confidence: 99%

“…[73][74][75][76][77] In one study, the use of ML predicted ASM regimens associated with improved outcomes and reduced costs due to lower healthcare utilization rates. 74 A recent study, using data from nearly 1800 adults with newly diagnosed epilepsy across four countries, developed a deep ML model 77 to predict the effectiveness of an ASM, defined as at least one year of seizure freedom. 75 The model considered lamotrigine, valproate, carbamazepine, levetiracetam, oxcarbazepine, topiramate, and phenytoin.…”

Section: Future Of Asm Therapymentioning

confidence: 99%

“…The AI platform could also help identify the most essential factors in predicting ASM treatment success by epilepsy type. 76 , 77 Wu et al 77 developed an AI model to predict response to ASMs in patients with idiopathic generalized epilepsy. They found that the number of seizure types and pretreatment seizure frequency were among the most effective predictors of ASM response.…”

Section: Introductionmentioning

confidence: 99%

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The evolution of antiseizure medication therapy selection in adults: Is artificial intelligence -assisted antiseizure medication selection ready for prime time?

Gunasekera,

Sirven,

Feyissa

2023

J Cent Nerv Syst Dis

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Antiseizure medications (ASMs) are the mainstay of symptomatic epilepsy treatment. The primary goal of pharmacotherapy with ASMs in epilepsy is to achieve complete seizure remission while minimizing therapy-related adverse events. Over the years, more ASMs have been introduced, with approximately 30 now in everyday use. With such a wide variety, much guidance is needed in choosing ASMs for initial therapy, subsequent replacement monotherapy, or adjunctive therapy. The specific ASMs are typically tailored by the patient’s related factors, including epilepsy syndrome, age, sex, comorbidities, and ASM characteristics, including the spectrum of efficacy, pharmacokinetic properties, safety, and tolerability. Weighing these key clinical variables requires experience and expertise that may be limited. Furthermore, with this approach, patients may endure multiple trials of ineffective treatments before the most appropriate ASM is found. A more reliable way to predict response to different ASMs is needed so that the most effective and tolerated ASM can be selected. Soon, alternative approaches, such as deep machine learning (ML), could aid the individualized selection of the first and subsequent ASMs. The recognition of epilepsy as a network disorder and the integration of personalized epilepsy networks in future ML platforms can also facilitate the prediction of ASM response. Augmenting the conventional approach with artificial intelligence (AI) opens the door to personalized pharmacotherapy in epilepsy. However, more work is needed before these models are ready for primetime clinical practice.

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Hybrid cuckoo finch optimisation based machine learning classifier for seizure prediction using EEG signals in IoT network

Kapoor

Nagpal

2023

Cluster Comput

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Machine learning model to predict the efficacy of antiseizure medications in patients with familial genetic generalized epilepsy

Cited by 6 publications

References 37 publications

Predictive models of epilepsy outcomes

Predictive models of epilepsy outcomes

The evolution of antiseizure medication therapy selection in adults: Is artificial intelligence -assisted antiseizure medication selection ready for prime time?

Hybrid cuckoo finch optimisation based machine learning classifier for seizure prediction using EEG signals in IoT network

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