US 12,246,188 B2
Selection of probability thresholds for generating cardiac arrhythmia notifications
Siddharth Dani, Minneapolis, MN (US); Tarek D. Haddad, Minneapolis, MN (US); Donald R. Musgrove, Minneapolis, MN (US); Andrew Radtke, Minneapolis, MN (US); Niranjan Chakravarthy, Singapore (SG); Rodolphe Katra, Blaine, MN (US); and Lindsay A. Pedalty, Minneapolis, MN (US)
Assigned to Medtronic, Inc., Minneapolis, MN (US)
Filed by Medtronic, Inc., Minneapolis, MN (US)
Filed on Jan. 18, 2023, as Appl. No. 18/155,803.
Application 18/155,803 is a continuation of application No. 16/850,833, filed on Apr. 16, 2020, granted, now 11,583,687.
Claims priority of provisional application 62/843,707, filed on May 6, 2019.
Prior Publication US 2023/0149726 A1, May 18, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. A61N 1/39 (2006.01); A61N 1/365 (2006.01); G16H 10/60 (2018.01); G16H 40/63 (2018.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01)
CPC A61N 1/3956 (2013.01) [A61N 1/36592 (2013.01); G16H 10/60 (2018.01); G16H 40/63 (2018.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01)] 18 Claims
OG exemplary drawing
 
1. A method comprising:
generating, by a computing system that comprises processing circuitry and one or more storage media, a set of sample probability values by applying a machine learning model to a sample set of patient data, wherein:
the machine learning model is trained using patient data for a plurality of patients,
the sample set comprises a plurality of temporal windows, and
for each respective temporal window of the plurality of temporal windows, the machine learning model is configured to determine a respective probability value in the set of sample probability values that indicates a probability that a cardiac arrhythmia occurred during the respective temporal window;
generating, by the computing system, graphical data based on the sample probability values;
outputting, by the computing system, a user interface for display on a display device, the user interface comprising the graphical data;
receiving, by the computing system, via the user interface, an indication of user input to select a probability threshold for a patient;
receiving, by the computing system, patient data for the patient, wherein the patient data for the patient is collected by one or more medical devices;
applying, by the computing system, the machine learning model to the patient data for the patient to determine a current probability value that indicates a probability that the patient has experienced an occurrence of the cardiac arrhythmia;
determining, by the computing system, that the current probability value exceeds the probability threshold for the patient;
in response to determining that the current probability value is greater than or equal to the probability threshold for the patient, generating, by the computing system, a notification indicating that the patient has likely experienced the occurrence of the cardiac arrhythmia; and
presenting, by the computing system, data indicating an anticipated review burden versus an anticipated diagnostic yield for the probability threshold for the patient.