Year: 2012
Type: article
Study design: Observational Study
Abstract: 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.
Source: Acta Neurologica Scandinavica
DOI: 10.1111/ane.12002
Institution: Cincinnati Children's Hospital Medical Center
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W1987176315