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Keywords:

  • epilepsy;
  • pediatric neurology;
  • decision support;
  • anti-epileptic drug selection;
  • artificial intelligence

Objective

To identify which clinical characteristics are important to include in clinical decision support systems developed for Antiepileptic Drug (AEDs) selection.

Methods

Twenty-three epileptologists from the Childhood Absence Epilepsy network completed a survey related to AED selection. Using cluster analysis their responses where classified into subject matter groups and weighted for importance.

Results

Five distinct subject matter groups were identified and their relative weighting for importance were determined: disease characteristics (weight 4.8 ± 0.049), drug toxicities (3.82 ± 0.098), medical history (3.12 ± 0.102), systemic characteristics (2.57 ± 0.048) and genetic characteristics (1.08 ± 0.046).

Conclusion

Research about prescribing patterns exists but research on how such data can be used to train advanced technology is novel. As machine learning algorithms becomes more and more prevalent in clinical decisions support systems, developing methods for determining which data should be part of those algorithms is equally important.