Association Analysis for Implantable Medical Device Programming
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Solution Overview
Problem
Modern implantable medical devices (IMDs) face challenges in optimizing parameter settings, as multiple parameters interact to deliver effective therapy, and existing methods lack a systematic approach to derive optimal settings based on collective data from multiple devices.
Innovation Solution
The method involves gathering parameter and outcome data from multiple IMDs, performing association analysis to form association rules, and suggesting parameter choices to users based on these rules, utilizing a server to derive and apply these rules for programming IMDs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple parameters are adjusted to optimize therapy delivery, then therapeutic efficacy is improved, but programming complexity increases
Solution Approach 1:
The system automatically generates parameter recommendations by analyzing collective data from multiple IMDs, allowing the programming device to serve itself rather than requiring manual analysis of complex parameter interactions by the user
Solution Approach 2:
The system uses outcome data from previously programmed devices to create feedback loops, where association analysis of parameter-outcome relationships provides evidence-based recommendations for optimal parameter settings in new programming scenarios
2Adaptability or versatility
If manual parameter programming is performed, then customization to individual patient needs is improved, but time consumption increases
Solution Approach 1:
The system performs preliminary analysis of collective data from multiple devices to pre-derive association rules before the actual programming occurs, so that when programming a new patient, the system can quickly retrieve and apply relevant parameter recommendations without time-consuming analysis during the programming session
Solution Approach 2:
The system creates virtual copies of successfully programmed parameters from multiple IMDs, allowing the programmer to quickly reference and adapt proven parameter settings from similar cases without manually reconfiguring each parameter
3Reliability
If association analysis is performed on collective data, then parameter optimization is improved, but data processing complexity increases
Solution Approach 1:
The system introduces an intermediary data processing layer that automatically performs association analysis on collective IMD data, separating the complex analytical work from the user interface and handling data processing complexity in a dedicated processing layer
Data Source
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AI summary
Embodiments of the invention are directed to systems and methods for programming implantable medical devices, amongst other things. In an embodiment, the invention includes a method of programming an implantable medical device. The method can include gathering parameter data representing a set of previously programmed parameter values from a plurality of implanted medical devices. The method can further include performing association analysis on the parameter data to form a set of association rules. The method can further include suggesting parameter choices to a system user regarding a specific patient based on the set of association rules. In an embodiment, the invention can include a medical system including a server configured to perform association analysis on a set of data representing previously programmed parameter values from a plurality of implanted medical devices to derive a set of association rules. Other embodiments are also included herein.