Machine Learning Approach to Optimizing Combined Stimulation and Medication Therapies for Parkinson's Disease

- Clinical decision support system for the treatment of Parkinson's disease patients
- Model-based balance between stimulation parameter settings and medication dosages
- Conceptual implementation of “Big Data Analytics” in Parkinson's disease

BackgroundDeep brain stimulation (DBS) of the subthalamic region is an established therapy for advanced Parkinson's disease (PD). However, patients often require time-intensive post-operative management to balance their coupled stimulation and medication treatments. Given the large and complex parameter space associated with this task, we propose that clinical decision support systems (CDSS) based on machine learning algorithms could assist in treatment optimization.ObjectiveDevelop a proof-of-concept implementation of a CDSS that incorporates patient-specific details on both stimulation and medication.MethodsClinical data from 10 patients, and 89 post-DBS surgery visits, were used to create a prototype CDSS. The system was designed to provide three key functions: 1) information retrieval; 2) visualization of treatment, and; 3) recommendation on expected effective stimulation and drug dosages, based on three machine learning methods that included support vector machines, Naïve Bayes, and random forest.ResultsMeasures of medication dosages, time factors, and symptom-specific pre-operative response to levodopa were significantly correlated with post-operative outcomes (P < 0.05) and their effect on outcomes was of similar magnitude to that of DBS. Using those results, the combined machine learning algorithms were able to accurately predict 86% (12/14) of the motor improvement scores at one year after surgery.ConclusionsUsing patient-specific details, an appropriately parameterized CDSS could help select theoretically optimal DBS parameter settings and medication dosages that have potential to improve the clinical management of PD patients.