Predicting postoperative epilepsy surgery satisfaction in adults using the 19-item Epilepsy Surgery Satisfaction Questionnaire and machine learning.
Colin Bruce JosephsonJordan D T EngbersTolulope T SajobiSandra WahbyOluwaseyi A LawalMark R KeezerDang K NguyenKristina MalmgrenMark J AtkinsonWalter J HaderSophia MacrodimitrisScott B PattenColin B JosephsonRuby SharmaShaily SinghYves StarreveldSamuel WiebePublished in: Epilepsia (2021)
Machine learning applied postoperatively to the ESSQ-19 can be used to predict surgical satisfaction. This algorithm, once externally validated, can be used in clinical settings by fixing immutable clinical characteristics and adjusting hypothesized postoperative variables, to counsel patients at an individual level on how satisfied they will be with differing surgical outcomes.
Keyphrases
- machine learning
- minimally invasive
- coronary artery bypass
- patients undergoing
- artificial intelligence
- big data
- deep learning
- psychometric properties
- surgical site infection
- cross sectional
- percutaneous coronary intervention
- acute coronary syndrome
- coronary artery disease
- atrial fibrillation
- patient reported
- neural network