The evolution of antiseizure medication therapy selection in adults: Is artificial intelligence -assisted antiseizure medication selection ready for prime time?
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...
| Publicado en: | Journal of Central Nervous System Disease pp. 1 - 14 |
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| Autores principales: | , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
10/18/2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=173048744&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173048744 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11795735 B3L8 jtl: Journal of Central Nervous System Disease issn: 11795735 maglogo: Y pubinfo: dt: 10/18/2023 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 173048744 173048744 173048744 10.1177/11795735231209209 173048744 ppf: 1 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: The evolution of antiseizure medication therapy selection in adults: Is artificial intelligence -assisted antiseizure medication selection ready for prime time? aug: au: Gunasekera, Charlene L. Sirven, Joseph I. Feyissa, Anteneh M. affil: Department of Neurology, 156400 Mayo Clinic, Jacksonville, FL, USA sug: subj: Anticonvulsants Therapeutic Use Seizures Seizures Prevention and Control Comorbidity Morbidity Prevention and Control Hospital Mortality Prevention and Control Human Artificial Intelligence Central Nervous System Nervous System Diseases Drug Therapy Adult Adult: 19-44 years ab: 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. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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