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

Abstract Background Deep 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 an...

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Bibliographic Details
Published in:Brain stimulation 2015-11, Vol.8 (6), p.1025-1032
Main Authors: Shamir, Reuben R, Dolber, Trygve, Noecker, Angela M, Walter, Benjamin L, McIntyre, Cameron C
Format: Article
Language:English
Subjects:
Adult
Aged
Antiparkinson Agents - administration & dosage
Bayes Theorem
Clinical decision support system
Combined Modality Therapy - methods
Combined Modality Therapy - standards
Databases, Factual
Deep brain stimulation
Deep Brain Stimulation - methods
Deep Brain Stimulation - standards
Female
Follow-Up Studies
Humans
Levodopa - administration & dosage
Machine Learning
Male
Middle Aged
Neurology
Parkinson Disease - diagnosis
Parkinson Disease - therapy
Parkinson's disease
Retrospective Studies
Subthalamic Nucleus - physiology
Time Factors
Treatment Outcome
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Summary:Abstract Background Deep 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. Objective Develop a proof-of-concept implementation of a CDSS that incorporates patient-specific details on both stimulation and medication. Methods Clinical 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. Results Measures of medication dosages, time factors, and symptom-specific pre-operative response to levodopa were significantly correlated with post-operative outcomes ( P  
ISSN:1935-861X
1876-4754
DOI:10.1016/j.brs.2015.06.003