公开(公告)号：US11837343B2

公开(公告)日：2023-12-05

申请号：US15966347

申请日：2018-04-30

Applicant: Merative US L.P.

Inventor： Eric W. Brown ,  Maria Eleftheriou ,  Anca Sailer ,  Ching-Huei Tsou

IPC: G06Q50/00 ,  G16H10/60 ,  G06F16/31 ,  G06N20/00 ,  G16H50/20

CPC classification number: G16H10/60 ,  G06F16/313 ,  G06N20/00 ,  G16H50/20

Abstract: A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement a repetitive portion identification and weighting engine. A machine learning model is trained for weighting repetitive portions of patient electronic medical records (EMRs). A repetitive portion identification component applies a plurality of templates to clinical notes of a patient EMR to identify one or more candidate portions that match at least one of the plurality of templates. A content analysis component performs content analysis on the one or more candidate portions to determine whether each given candidate portion is relevant. A weighting component assigns a relative weight to each given candidate portion based on relevance. A cognitive summary graphical user interface (GUI) generation component generates cognitive summary reflecting at least a subset of the one or more candidate portions of the patient EMR. The mechanism outputs the cognitive summary in a GUI to a user.

公开(公告)号：US20240053307A1

公开(公告)日：2024-02-15

申请号：US18383180

申请日：2023-10-24

Applicant: Merative US L.P.

Inventor： Eric W. Brown ,  Maria Eleftheriou ,  Anca Sailer ,  Ching-Huei Tsou

IPC: G01N30/80 ,  G01N30/32 ,  G01N30/60

CPC classification number: G01N30/80 ,  G01N30/32 ,  G01N30/6073 ,  G01N2030/328 ,  G01N2030/324 ,  G01N2030/326 ,  G01N2030/025

Abstract: A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement a repetitive portion identification and weighting engine. A machine learning model is trained for weighting repetitive portions of patient electronic medical records (EMRs). A repetitive portion identification component applies a plurality of templates to clinical notes of a patient EMR to identify one or more candidate portions that match at least one of the plurality of templates. A content analysis component performs content analysis on the one or more candidate portions to determine whether each given candidate portion is relevant. A weighting component assigns a relative weight to each given candidate portion based on relevance. A cognitive summary graphical user interface (GUI) generation component generates cognitive summary reflecting at least a subset of the one or more candidate portions of the patient EMR. The mechanism outputs the cognitive summary in a GUI to a user.