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Selecting anti‐epileptic drugs: a pediatric epileptologist's view, a computer's view
Author(s) -
Pestian J.,
Matykiewicz P.,
HollandBouley K.,
Standridge S.,
Spencer M.,
Glauser T.
Publication year - 2013
Publication title -
acta neurologica scandinavica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.967
H-Index - 95
eISSN - 1600-0404
pISSN - 0001-6314
DOI - 10.1111/ane.12002
Subject(s) - weighting , subject matter , epilepsy , selection (genetic algorithm) , medicine , disease , cluster (spacecraft) , antiepileptic drug , computer science , artificial intelligence , machine learning , psychiatry , data science , psychology , pedagogy , curriculum , radiology , programming language
Objective To identify which clinical characteristics are important to include in clinical decision support systems developed for A ntiepileptic D rug ( AED s) selection. Methods Twenty‐three epileptologists from the C hildhood A bsence E pilepsy 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.