US 11,749,387 B2
Deduplication of medical concepts from patient information
Tanveer F. Syeda-Mahmood, Cupertino, CA (US); and Chaitanya Shivade, San Jose, CA (US)
Filed by Merative US L.P., Ann Arbor, MI (US)
Filed on Jun. 17, 2021, as Appl. No. 17/350,441.
Application 17/350,441 is a continuation of application No. 16/150,414, filed on Oct. 3, 2018, granted, now 11,081,216.
Prior Publication US 2021/0313025 A1, Oct. 7, 2021
This patent is subject to a terminal disclaimer.
Int. Cl. G16H 10/60 (2018.01); G16H 50/70 (2018.01); G16H 70/60 (2018.01); G16H 15/00 (2018.01)
CPC G16H 10/60 (2018.01) [G16H 15/00 (2018.01); G16H 50/70 (2018.01); G16H 70/60 (2018.01)] 20 Claims
OG exemplary drawing
 
1. A method, in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to configure the processor to implement a patient summary generation engine, the method comprising:
configuring computer natural language processing logic to extract, from content of a patient electronic medical records (EMRs), portions of the content corresponding to the at least one of terms, phrases, or medical codes specified in clinical data category specific knowledge resource data structures corresponding to a clinical data category, selected from a plurality of clinical data categories, based on the clinical data category being specified in a received request;
parsing, by the configured computer natural language processing logic, a patient electronic medical record (EMR) to extract a plurality of instances of a medical concept, wherein at least two instances of the medical concept utilize different representations of the medical concept in the patient electronic medical record;
performing, by the patient summary generation engine, a similarity analysis between a plurality of combinations of the instances of the medical concept to thereby calculate, for each combination of instances in the plurality of combinations of instances of the medical concept, a similarity metric value;
clustering, by the patient summary generation engine, the instances of the medical concept based on the calculated similarity metric values for each combination in the plurality of combinations of the instances of the medical concept to thereby generate one or more clusters;
selecting, by the patient summary generation engine, a representative instance of the medical concept from each cluster in the one or more clusters; and
generating, by the patient summary generation engine, a summary output of the patient EMR comprising the selected representative instances of the medical concept from each cluster.