	US 11,894,117 B1
	Discovering context-specific complexity and utilization sequences
Douglas S. McNair, Seattle, WA (US)
Assigned to Cerner Innovation, Inc., Kansas City, MO (US)
Filed by CERNER INNOVATION, INC., Kansas City, KS (US)
Filed on Sep. 7, 2021, as Appl. No. 17/467,770.
Application 15/855,720 is a division of application No. 14/281,593, filed on May 19, 2014, granted, now 10,769,241, issued on Sep. 8, 2020.
Application 17/467,770 is a continuation of application No. 15/855,720, filed on Dec. 27, 2017, granted, now 11,145,396.
Application 14/281,593 is a continuation in part of application No. 14/209,568, filed on Mar. 13, 2014, abandoned.
Application 14/209,568 is a continuation in part of application No. 14/175,750, filed on Feb. 7, 2014, granted, now 10,946,311, issued on Mar. 16, 2021.
Claims priority of provisional application 61/824,377, filed on May 17, 2013.
Claims priority of provisional application 61/762,178, filed on Feb. 7, 2013.
This patent is subject to a terminal disclaimer.
Int. Cl. G16H 20/10 (2018.01); G16H 10/60 (2018.01); G16H 50/20 (2018.01)

CPC G16H 20/10 (2018.01) [G16H 10/60 (2018.01); G16H 50/20 (2018.01)]

31 Claims

1. A method, comprising:

for a candidate patient, determining, by a computer processor, a set of health records stored on one or more data stores distributed across multiple Physical locations for a population of reference patients having conditions, multiple episodes, or encounters over a period of time similar to the candidate patient, wherein each reference patient health record forms a reference set of clinical event sequences;

receiving, by the computer processor, candidate patient information associated with the candidate patient;

creating a candidate patient data frame based on the received candidate patient information; and

determining, by the computer processor, an inter-event pattern present in the set of health records of the reference patients based on:

a comparison of the candidate patient data frame and one or more of the reference sets of clinical event sequences,

a matching set of clinical event sequences, and

a set of timeseries trajectory clusters determined by:

(a) performing, on at least one of the one or more data stores, a database scan that uses a sparse-matrix approach to decompose an original search lattice into sub-lattices that are processed independently in distributed memory associated with multiple parallel processors;

(b) computing and applying a multi-cluster model; and

wherein each timeseries trajectory cluster has a centroid.