Bayesian Risk Projection for Personalized IMD Therapy Settings
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Solution Overview
Problem
Existing medical device systems struggle to provide personalized risk assessments for individual patients, relying on population-based data rather than customized calculations for implantable medical devices, which can lead to suboptimal therapy delivery configurations.
Innovation Solution
Utilizing Bayesian Network models trained on patient-specific data to generate personalized risk assessments and therapy configuration recommendations for implantable medical devices, such as ICDs and CRT-Ds, incorporating patient and device-specific inputs and unique use conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If population-based data is used for risk assessment, then generalizability is improved, but personalization is worsened
Solution Approach 1:
The patent segments the patient population into distinct subgroups based on clinical characteristics, device parameters, and outcome data. By creating segmented cohorts rather than using a single population-based model, the system achieves both personalization for individual patients and generalizability through statistically powered subgroup analyses, resolving the contradiction between personalized adaptation and measurement precision.
2Adaptability or versatility
If customized risk calculations are performed for each patient, then personalization is improved, but computational complexity is worsened
Solution Approach 1:
The patent performs preliminary actions by pre-calculating risk models for multiple patient subgroups and device configurations before clinical deployment. During actual use, the system simply matches the patient's characteristics to pre-computed models rather than performing complex real-time calculations, thereby achieving customization without excessive computational complexity.
Solution Approach 2:
The system manages complexity by changing parameters in a structured manner - using discrete categorical variables for patient subgroups and device settings rather than continuous complex parameters. This parameter discretization allows customized risk calculations while maintaining computational tractability through standardized model inputs.
3Adaptability or versatility
If multiple device configurations are evaluated, then adaptability is improved, but analysis time is worsened
Solution Approach 1:
The patent applies local quality by providing tailored risk assessments and configuration recommendations specific to each patient's characteristics and clinical scenario. Rather than evaluating all possible configurations universally, the system focuses computational resources on the most relevant configurations for each patient, achieving adaptability while reducing analysis time through targeted evaluation.
Data Source
AI summary
An example system includes a memory configured to store cardiac data associated with a patient population within a Bayesian network structure describing cardiac health events for the patient population. The system may apply new patient data to an Artificial Intelligence model (AI model) trained using the Bayesian network structure to output a risk-benefit assessment specific to a particular patient implantable medical device (IMD). The output may include selectable configuration recommendations and a corresponding risk probability for each configuration. Subsequent to the output, the system may receive clinician input selecting one of the configuration recommendations for the patient IMD. Responsive to receipt of the clinician input, the system may configure delivery of a therapy via the patient IMD using the one of the multiple configuration recommendations selected according to the clinician input.