US 12,266,431 B2
Machine learning engine and rule engine for document auto-population using historical and contextual data
Tanmoy Mukherjee, Kolkata (IN); Shrilata Mondal, Guskara Bardhaman (IN); Debendra Kar, Marathalli Bangalore (IN); and Damodar Reddy Karra, Telangana (IN)
Assigned to Cerner Innovation, Inc., Kansas City, MO (US)
Filed by CERNER INNOVATION, INC., Kansas City, KS (US)
Filed on Apr. 5, 2021, as Appl. No. 17/221,981.
Prior Publication US 2022/0319646 A1, Oct. 6, 2022
Int. Cl. G16H 10/60 (2018.01); G06F 40/174 (2020.01); G06F 40/20 (2020.01)
CPC G16H 10/60 (2018.01) [G06F 40/174 (2020.01); G06F 40/20 (2020.01)] 40 Claims
OG exemplary drawing
 
34. A method, comprising:
generating a combined vector, associated with healthcare information, based on a first set of historical text data and based further on a second set of historical contextual data, the first set of historical text data differing from the second set of historical contextual data;
generating a plurality of clusters based on an electronic machine-learning clustering model using the combined vector as input, wherein:
(a) the electronic machine-learning clustering model is trained based on data associated with instances of first information selected from a group comprising the first set of historical text data and the second set of historical contextual data, and
(b) the plurality of clusters includes the first set of historical text data and further include the second set of historical contextual data;
identifying a primary cluster in the plurality of clusters that is a best match to the combined vector;
performing natural language processing on second information associated with the primary cluster;
based at least on the natural language processing:
assigning a relevance score, for at least a portion of a plurality of text blocks in the primary cluster, corresponding to a respective relevance of at least a portion of the plurality of text blocks to information associated with at least a portion of the combined vector;
identifying a primary text block in the plurality of text blocks of the primary cluster having a highest relevance score; and
in response to the identifying of the primary text block in the plurality of text blocks of the primary cluster having the highest relevance score:
communicating the primary text block for display as a recommended selection for automatic population into at least a narrative field, of a healthcare related electronic document, configured to receive free-form narrative text.