US 11,809,826 B2
Assertion detection in multi-labelled clinical text using scope localization
Rajeev Bhatt Ambati, Plainsboro, NJ (US); Oladimeji Farri, Upper Saddle River, NJ (US); and Ramya Vunikili, Secaucus, NJ (US)
Assigned to Siemens Healthcare GmbH, Erlangen (DE)
Filed by Siemens Healthcare GmbH, Erlangen (DE)
Filed on Nov. 17, 2020, as Appl. No. 16/949,838.
Claims priority of provisional application 62/946,187, filed on Dec. 10, 2019.
Prior Publication US 2021/0174027 A1, Jun. 10, 2021
Int. Cl. G06F 40/00 (2020.01); G06F 40/30 (2020.01); G16H 50/20 (2018.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G06N 3/08 (2023.01); G06F 40/279 (2020.01); G16H 70/60 (2018.01); A61B 6/00 (2006.01); A61B 6/03 (2006.01)
CPC G06F 40/30 (2020.01) [G06F 40/279 (2020.01); G06N 3/08 (2013.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 70/60 (2018.01); A61B 6/032 (2013.01); A61B 6/503 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for assertion detection from clinical text in a medical system, the method comprising:
inputting the clinical text to a machine learning model;
identifying both a scope as a word group box and an assertion class for the word group box from the clinical text, the machine learning model identifying both the word group box and the assertion class in response to the inputting; and
generating an image showing words of the word group box and the assertion class for the words.
 
12. A system for assertion detection from clinical text, the system comprising:
a memory configured to store the clinical text for a patient;
a processor configured to separate the clinical text into multiple assertions, the clinical text separated by a machine learning model configured to localize a scope in the clinical text for each of the assertions and a class for each of the assertions, the machine learning model having been trained with multi-labelled data as a multiple-task model; and
a display configured to indicate the scope and class for each assertion.