Abstract: |
Example apparatus and methods concern a next generation clinical decision support system (ngCDSS) for the management of neurological conditions (e.g., advanced Parkinson's disease (PD)). Conventional coupled adjustment of pharmacologic therapy and stimulation parameter settings is a time-consuming process that sometimes yields sub-optimal outcomes. Example ngCDSS use a machine learning trained function that relates deep brain stimulation (DBS) parameters, medication dosages, and patient-specific pre and post operative clinical data with actual treatment outcomes for a population of previously treated patients. Example ngCDSS incorporate image-based patient-specific computer models of the estimated stimulation volume of tissue stimulated by DBS in a multi-linear regression analysis to produce a predictor function that is highly correlated with actual outcomes. Example ngCDSS facilitate predicting the outcomes of a combined pharmacologic-DBS therapy, which in turn facilitate optimizing patient-specific treatment for improved benefits with minimal adverse effects. |
Inventor: |
McIntyre, Cameron (Cleveland Heights, OH, US); SHamir, Reuben R. (Cleveland, OH, US); Walter, Benjamin L. (Cleveland, OH, US) |
Applicant: |
Case Western Reserve University (Cleveland, OH, US) |
Face Assignee: |
Case Western Reserve University (Cleveland, OH, US) |
Filed: |
2015-05-29 |
Issued: |
2017-09-19 |
Claims: |
9 |
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US9764136
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1. An apparatus for selecting treatment parameters for a patient, comprising:
(2)
(6)
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