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.

Abstract: Example apparatus and methods concern a clinical decision support system for the selection of candidates. A clinical decision support system includes a candidate data logic that receives electronic data that identifies candidate data, including symptom and non-symptom data, for a candidate. The clinical decision support system also includes a scoring logic that generates a score for the candidate based, at least in part, on a set of rules being applied to the candidate data. The set of rules is based on patient data of a set of patients. The clinical decision support system further includes an identification logic that identifies a personalized treatment for the candidate based, at least in part, on the score.

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.