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Machine learning models for decision support in epilepsy management: A critical review
Eliot D Smolyansky1, Haris Hakeem2, Zongyuan Ge3
1Melbourne Medical School, The University of Melbourne, Parkville, Victoria 3010, Australia.
Purpose:
There remain major challenges for the clinician in managing patients with epilepsy effectively. Choosing anti-seizure medications (ASMs) is subject to trial and error. About one-third of patients have drug-resistant epilepsy (DRE). Surgery may be considered for selected patients, but time from diagnosis to surgery averages 20 years. We reviewed the potential use of machine learning (ML) predictive models as clinical decision support tools to help address some of these issues.
Methods:
We conducted a comprehensive search of Medline and Embase of studies that investigated the application of ML in epilepsy management in terms of predicting ASM responsiveness, predicting DRE, identifying surgical candidates, and predicting epilepsy surgery outcomes. Original articles addressing these 4 areas published in English between 2000 and 2020 were included.
Results:
We identified 24 relevant articles: 6 on ASM responsiveness, 3 on DRE prediction, 2 on identifying surgical candidates, and 13 on predicting surgical outcomes. A variety of potential predictors were used including clinical, neuropsychological, imaging, electroencephalography, and health system claims data. A number of different ML algorithms and approaches were used for prediction, but only one study utilized deep learning methods. Some models show promising performance with areas under the curve above 0.9. However, most were single setting studies (18 of 24) with small sample sizes (median number of patients 55), with the exception of 3 studies that utilized large databases and 3 studies that performed external validation. There was a lack of standardization in reporting model performance. None of the models reviewed have been prospectively evaluated for their clinical benefits.
Conclusion:
The utility of ML models for clinical decision support in epilepsy management remains to be determined. Future research should be directed toward conducting larger studies with external validation, standardization of reporting, and prospective evaluation of the ML model on patient outcomes.
Insights
Machine learning (ML) shows promise for epilepsy management, aiding in predicting anti-seizure medication response and surgical outcomes. However, further research with larger, validated studies is needed to confirm clinical utility.
Area of Science:
- Neurology
- Medical Informatics
- Artificial Intelligence
Background:
- Epilepsy management presents significant clinical challenges, including trial-and-error medication selection and long delays for surgical intervention.
- Approximately one-third of patients experience drug-resistant epilepsy (DRE), highlighting the need for improved treatment strategies.
- Current diagnostic and treatment pathways for epilepsy require enhanced decision support tools.
Purpose of the Study:
- To review the potential application of machine learning (ML) predictive models as clinical decision support tools in epilepsy management.
- To assess the use of ML in predicting anti-seizure medication (ASM) responsiveness and identifying patients with DRE.
- To evaluate ML's role in identifying surgical candidates and predicting epilepsy surgery outcomes.
Main Methods:
- A comprehensive literature search was conducted on Medline and Embase for studies published between 2000 and 2020.
- Included studies focused on ML applications in predicting ASM responsiveness, DRE, surgical candidacy, and surgical outcomes in epilepsy.
- Data sources included clinical, neuropsychological, imaging, electroencephalography, and health claims data.
Main Results:
- 24 relevant articles were identified, with a focus on predicting surgical outcomes (13 studies).
- A variety of ML algorithms were employed, with some models demonstrating high predictive performance (AUC > 0.9).
- Most studies were single-center with small sample sizes; limitations include lack of standardization and prospective evaluation.
Conclusions:
- The clinical utility of ML models for decision support in epilepsy management requires further determination.
- Future research should prioritize larger studies with external validation and prospective evaluation of ML models.
- Standardization in reporting model performance is crucial for advancing the field.
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