PassFlow: a multimodal workflow for predicting deep brain stimulation outcomes.
Maxime PeraltaClaire HaegelenPierre JanninJohn S H BaxterPublished in: International journal of computer assisted radiology and surgery (2021)
We presented a novel, machine learning-based pipeline to predict a variety of post-operative clinical outcomes of DBS for PD patients. PassFlow took into account various bio-markers, arising from different data modalities, showing high correlation coefficients for some scores from pre-operative data only. It indicates that many clinical outcomes of DBS can be predicted agnostic to the specific simulation parameters, as PassFlow has been validated without such stimulation-related information.
Keyphrases
- deep brain stimulation
- parkinson disease
- machine learning
- electronic health record
- obsessive compulsive disorder
- end stage renal disease
- big data
- newly diagnosed
- ejection fraction
- chronic kidney disease
- prognostic factors
- artificial intelligence
- pain management
- metabolic syndrome
- patient reported outcomes
- skeletal muscle
- insulin resistance