US 11,864,925 B2
Machine learning techniques for detecting splinting activity
Jon Kevin Muse, Thompsons Station, TN (US); Marilyn L. Gordon, Cherry Hill, NJ (US); Komal Khatri, Cedar Park, TX (US); and Gregory J. Boss, Saginaw, MI (US)
Assigned to UnitedHealth Group Incorporated, Minnetonka, MN (US)
Filed by UnitedHealth Group Incorporated, Minnetonka, MN (US)
Filed on Jul. 14, 2021, as Appl. No. 17/375,627.
Prior Publication US 2023/0020331 A1, Jan. 19, 2023
Int. Cl. A61B 5/00 (2006.01); G06N 20/00 (2019.01); G16H 40/67 (2018.01); G16H 50/70 (2018.01); A61B 5/113 (2006.01); G16H 50/30 (2018.01); A61B 5/08 (2006.01); G16H 50/20 (2018.01)
CPC A61B 5/7267 (2013.01) [A61B 5/0816 (2013.01); A61B 5/1135 (2013.01); A61B 5/4824 (2013.01); A61B 5/7275 (2013.01); G06N 20/00 (2019.01); G16H 40/67 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G16H 50/70 (2018.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
identifying, by one or more processors, observed breathing pattern sensory data for a monitored individual;
determining, by the one or more processors, an observed inspiration-expiration waveform pattern based at least in part on the observed breathing pattern sensory data;
generating, by the one or more processors and utilizing a splinting activity detection machine learning model, a predicted interruption score for the observed breathing pattern sensory data, wherein: the predicted interruption score (i) is generated based at least in part on the observed inspiration-expiration waveform pattern and one or more expected inspiration-expiration waveform patterns, and (ii) represents a likelihood that the observed inspiration-expiration waveform pattern represents splinting activity; and
initiating, by the one or more processors, the performance of one or more prediction-based actions based at least in part on the predicted interruption score.