Bayesian network-based risk projection for implantable medical devices

Patent metadata

Field	Value
Publication number	US-2026045332-A1
Application number	US-202519296273-A
Country	US
Kind code	A1
Filing date	Aug 11, 2025
Priority date	Aug 12, 2024
Publication date	Feb 12, 2026
Grant date	—

Abstract

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.

First claim

What is claimed is:

1 . A system comprising:
a memory configured to store cardiac data associated with a patient population within a Bayesian network structure, wherein the cardiac data describes cardiac health events for the patient population; and processing circuitry in communication with the memory, wherein the processing circuitry is configured to: receive new patient data describing at least one of a new cardiac health event determined by a patient implantable medical device (IMD), a record of an arrhythmia treatment applied by the patient IMD, or a programming configuration for the patient IMD; output a risk-benefit assessment specific to the patient IMD, wherein the risk-benefit assessment indicates multiple configuration recommendations for the patient IMD and a corresponding risk probability for each of the multiple configuration recommendations based at least in part on application of the new patient data to an Artificial Intelligence model (AI model) trained using the Bayesian network structure to describe cardiac health profiles for a patient population; subsequent to output of the risk-benefit assessment specific to the patient IMD, receive clinician input with a selection of one of the multiple configuration recommendations for the patient IMD; and responsive to receipt of the clinician input with the selection of the one of the multiple configuration recommendations for the patient IMD, configure delivery of a therapy via the patient IMD using the one of the multiple configuration recommendations selected according to the clinician input.

2 . The system of claim 1 , wherein the processing circuitry is further configured to:
recursively update the Bayesian network structure using the new patient data until the new patient data is represented within the Bayesian network structure.

3 . The system of claim 2 , wherein to recursively update the Bayesian network structure using the new patient data includes the processing circuitry further configured to:
update one or more conditional probability tables of the Bayesian network structure using the new patient data; and output the risk-benefit assessment specific to the patient IMD using the one or more conditional probability tables updated using the new patient data.

4 . The system of claim 1 , wherein the Bayesian network structure describes the cardiac health profiles for the patient population based on existing clinical trial data and existing field data.

5 . The system of claim 1 , wherein the cardiac health profiles include one or more of:
arrhythmia incidents within the patient population; arrhythmia treatment outcomes for the arrhythmia incidents within the patient population; IMD programming configurations for the patient population; or IMD product device types utilized by the patient population and device data.

6 . The system of claim 1 , wherein the processing circuitry is further configured to:
output the AI model, wherein the AI model is trained to generate patient-specific risk analysis for a patient based on the new patient data; and wherein the patient-specific risk analysis specifies at least one of:
risk probability for the patient using the patient IMD when surgically implanted;
risk probability for the patient using the patient IMD when configured with the one of the multiple configuration recommendations selected according to the clinician input; and
risk probability for the patient using the patient IMD when configured using the programming configuration previously utilized for the patient IMD.

7 . The system of claim 1 , wherein the processing circuitry is further configured to:
output the risk-benefit assessment specific to the patient IMD by applying methods including Maximum Likelihood Estimation (MLE) to the Bayesian network structure to identify the multiple configuration recommendations for the patient IMD and the corresponding risk probability for each of the multiple configuration recommendations probabilities based on frequencies observed in the new patient data within the Bayesian network structure.

8 . The system of claim 1 , wherein the patient IMD includes an implantable cardioverter defibrillator (ICD) type medical device.

9 . The system of claim 8 , wherein the programming configuration for the patient IMD includes ICD parameters including one or more of:
ICD arrhythmias detection thresholds; ICD high-energy electrical shock sequencing; ICD high-energy electrical shock timing; ICD high-energy electrical shock vectors; ICD arrhythmia therapy repetition parameters; ICD arrhythmia therapy success thresholds; or ICD arrhythmia therapy failure thresholds.

10 . The system of claim 8 , wherein the processing circuitry is further configured to:
train the AI model to generate as output, the risk-benefit assessment specific to the ICD type medical device, wherein to train the AI model includes the processing circuitry further configured to:
obtain a priori conditional probability tables representing existing clinical trial data and the existing field;
obtain training input parameters including observed health events within a subset of the patient population determined to have the ICD type medical device;
train the AI model to integrate the new patient data into the a priori conditional probability tables; and
update the AI model using the observed health events within the subset of the patient population determined to the ICD type medical device.

11 . The system of claim 1 , wherein the patient IMD includes an implantable cardiac resynchronization therapy defibrillator (CRT-D) type medical device.

12 . The system of claim 11 , wherein the programming configuration for the patient IMD includes CRT-D parameters including one or more of:
CRT-D therapy vectors; CRT-D therapy timing intervals; CRT-D electrical stimulation vectors; CRT-D electrical stimulation magnitude; or CRT-D electrical stimulation delivery timing.

13 . The system of claim 11 , wherein to train the AI model to generate as output, the risk-benefit assessment specific to the CRT-D type medical device, includes the processing circuitry further configured to:
obtain a priori conditional probability tables representing existing clinical trial data and the existing field; obtain training input parameters including observed health events within a subset of the patient population determined to have the CRT-D type medical device; train the AI model to integrate the new patient data into the a priori conditional probability tables; and update the AI model using the observed health events within the subset of the patient population determined to the CRT-D type medical device.

14 . The system of claim 1 , wherein to train the AI model to generate as output, the risk-benefit assessment specific to the patient IMD, includes the processing circuitry further configure to:
update the AI model based on a high voltage therapy (HVT) treatment configuration previously programmed into the patient IMD; output the risk-benefit assessment specific to the patient IMD including outputting from the updated AI model, one or more of:
projected efficacy and efficacy risk of the one of the multiple configuration recommendations selected according to the clinician input;
a listing of conditional probabilities, joint probabilities, marginal probabilities, or some combination thereof utilized by the Bayesian network structure in determining the corresponding risk probability for the one of the multiple configuration recommendations selected according to the clinician input;
a comparison of the corresponding risk probability

Assignees

• Medtronic Inc

Inventors

• Liu Yan
• Kuppuswamy Kiran

Classifications

• G16H50/70

  for mining of medical data, e.g. analysing previous cases of other patients · CPC title

• G16H50/20

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title

• G16H40/63

  for local operation · CPC title

• G16H20/40

  relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture · CPC title

• G16H10/60 Primary

  for patient-specific data, e.g. for electronic patient records · CPC title