US 12,437,856 B2
Machine learning techniques for parasomnia episode management
Ninad D. Sathaye, Bangalore (IN); Damian Kelly, Kildare (IE); Kimberly A. Vorse, Ketchum, ID (US); Atul Kumar, Bangalore (IN); Rahul Dutta, Bengaluru (IN); and Love Hasija, Sonepat (IN)
Assigned to UnitedHealth Group Incorporated, Minnetonka, MN (US)
Filed by UnitedHealth Group Incorporated, Minnetonka, MN (US)
Filed on Jan. 25, 2022, as Appl. No. 17/583,899.
Prior Publication US 2023/0238112 A1, Jul. 27, 2023
Int. Cl. G16H 20/70 (2018.01); A61B 5/00 (2006.01); G16H 50/20 (2018.01)
CPC G16H 20/70 (2018.01) [A61B 5/0006 (2013.01); A61B 5/4812 (2013.01); G16H 50/20 (2018.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for determining a recommended parasomnia reduction intervention for an ongoing sleep window, the computer-implemented method comprising:
determining, using one or more processors and a deep reinforcement machine learning model, and based at least in part on an ongoing sleep window representation of the ongoing sleep window, a recommended intervention vector that maximizes a value generation sub-model of the deep reinforcement machine learning model given an existing state defined by the ongoing sleep window representation, wherein each intervention vector that is supplied provided to the value generation sub-model comprises a plurality of operational parameter values for a defined parasomnia reduction intervention that is associated with the parasomnia reduction intervention;
determining, using the one or more processors, the recommended parasomnia reduction intervention based at least in part on the plurality of operational parameter values of the recommended intervention vector; and
performing, using the one or more processors, one or more prediction-based actions based at least in part on the recommended parasomnia reduction intervention.