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

Overview
Journal Brain Stimul
Publisher Elsevier
Specialty Neurology
Date 2015 Jul 5
PMID 26140956
Citations 24
Authors
Affiliations
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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 < 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.

Conclusions: Using 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.

Citing Articles

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Editorial: Insights into the use of deep brain stimulation as a treatment for Parkinson's disease and related conditions.

Liu Z, Zhou Y, Gao Y, Hu X Front Neurosci. 2023; 17:1322091.

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Machine learning for adaptive deep brain stimulation in Parkinson's disease: closing the loop.

Oliveira A, Coelho L, Carvalho E, Ferreira-Pinto M, Vaz R, Aguiar P J Neurol. 2023; 270(11):5313-5326.

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Early and High-Accuracy Diagnosis of Parkinson's Disease: Outcomes of a New Model.

Doumari S, Berahmand K, Ebadi M Comput Math Methods Med. 2023; 2023:1493676.

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A Combined Method for Diabetes Mellitus Diagnosis Using Deep Learning, Singular Value Decomposition, and Self-Organizing Map Approaches.

Nilashi M, Abumalloh R, Alyami S, Alghamdi A, Alrizq M Diagnostics (Basel). 2023; 13(10).

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