US 11,694,814 B1
Determining patient condition from unstructured text data
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 Jul. 29, 2020, as Appl. No. 16/942,066.
Application 16/942,066 is a continuation of application No. 14/960,249, filed on Dec. 4, 2015, granted, now 10,770,184.
Claims priority of provisional application 62/087,514, filed on Dec. 4, 2014.
This patent is subject to a terminal disclaimer.
Int. Cl. G16H 50/50 (2018.01); G16H 10/60 (2018.01)
CPC G16H 50/50 (2018.01) [G16H 10/60 (2018.01)] 20 Claims
OG exemplary drawing
 
1. Non-transitory computer-readable media having computer-executable instructions embodied thereon that when executed, facilitate a method for determining one or more patient conditions from unstructured text data, the method comprising:
receiving a structured topic modeling (STM) model associating terms with metadata labels;
receiving a set of clusters comprising an association of one or more terms and one or more metadata labels;
receiving a set of candidate conditions associated with each cluster of the set of clusters based on the association of one or more terms and one or more metadata labels;
receiving unstructured clinical narratives associated with a particular patient;
determining, using the STM model and the received unstructured clinical narratives, a likely cluster membership of the particular patient in one or more of a cluster of the set of clusters, wherein the likely cluster membership is determined by calculating a quantitative lexical distance between the unstructured clinical narrative associated with the particular patient and the set of candidate conditions; and
storing the likely cluster membership of the particular patient in a data store.