51. Machine Learning Approach to Optimizing Combined Stimulation and Medication Therapies for Parkinson's Disease
- Author
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Reuben R. Shamir, Cameron C. McIntyre, Angela M. Noecker, Trygve Dolber, and Benjamin L. Walter
- Subjects
Adult ,Male ,Levodopa ,Deep brain stimulation ,Parkinson's disease ,Time Factors ,Databases, Factual ,medicine.medical_treatment ,Deep Brain Stimulation ,Biophysics ,Clinical decision support system ,Machine learning ,computer.software_genre ,Article ,lcsh:RC321-571 ,Task (project management) ,Antiparkinson Agents ,Machine Learning ,Naive Bayes classifier ,Subthalamic Nucleus ,medicine ,Humans ,lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry ,Aged ,Retrospective Studies ,business.industry ,General Neuroscience ,Bayes Theorem ,Parkinson Disease ,Middle Aged ,medicine.disease ,Combined Modality Therapy ,Random forest ,Support vector machine ,Treatment Outcome ,Female ,Neurology (clinical) ,Artificial intelligence ,business ,computer ,medicine.drug ,Follow-Up Studies - 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
- Published
- 2015