TY - GEN

T1 - The Use of Model-Theoretical Methods for Automated Knowledge Extraction from Medical Texts

AU - Pogodin, Ruslan S.

AU - Palchunov, Dmitry

N1 - Funding Information: The research was funded by RFBR and Novosibirsk region, project number 20-47-540005. The study was carried out within the framework of the state contract of the Sobolev Institute of Mathematics (project no. 0314-2019-0002). Publisher Copyright: © 2021 IEEE.

PY - 2021/6/30

Y1 - 2021/6/30

N2 - The paper is devoted to the application of model-theoretical methods for extraction of knowledge from medical texts and documents and its formal representation. The aim of the work is to automate the filling of knowledge bases of the IACPaaS platform using knowledge from texts of disease descriptions. IACPaaS is a cloud platform for the development, management and remote use of intelligent cloud services. The peculiarities of disease description texts are the presence of medical word terms (such as 'blood pressure') and the abundance of sentences with clauses and homogeneous sentence members. To solve the problem of knowledge extraction, methods of transforming natural language sentences into quantifier-free formulas of the first-order predicate logic are used. Knowledge extracted from texts is formalized in the form of sets of atomic sentences that form fragments of atomic diagrams of algebraic systems. Further, a knowledge tree is built from the fragments of atomic diagrams, which serves as an intermediate representation of knowledge for subsequent translation into the format of IACPaaS information resources. The software system allows medical workers to fill knowledge bases with descriptions of diseases in shorter time, and gives the opportunity to check the consistency of the obtained formal specifications automatically.

AB - The paper is devoted to the application of model-theoretical methods for extraction of knowledge from medical texts and documents and its formal representation. The aim of the work is to automate the filling of knowledge bases of the IACPaaS platform using knowledge from texts of disease descriptions. IACPaaS is a cloud platform for the development, management and remote use of intelligent cloud services. The peculiarities of disease description texts are the presence of medical word terms (such as 'blood pressure') and the abundance of sentences with clauses and homogeneous sentence members. To solve the problem of knowledge extraction, methods of transforming natural language sentences into quantifier-free formulas of the first-order predicate logic are used. Knowledge extracted from texts is formalized in the form of sets of atomic sentences that form fragments of atomic diagrams of algebraic systems. Further, a knowledge tree is built from the fragments of atomic diagrams, which serves as an intermediate representation of knowledge for subsequent translation into the format of IACPaaS information resources. The software system allows medical workers to fill knowledge bases with descriptions of diseases in shorter time, and gives the opportunity to check the consistency of the obtained formal specifications automatically.

KW - clinical decision support system

KW - expert system

KW - knowledge extraction

KW - knowledge integration

KW - model-theoretical methods

KW - natural language processing

KW - ontological model

KW - ontology

KW - text analysis

UR - http://www.scopus.com/inward/record.url?scp=85113540901&partnerID=8YFLogxK

U2 - 10.1109/EDM52169.2021.9507606

DO - 10.1109/EDM52169.2021.9507606

M3 - Conference contribution

AN - SCOPUS:85113540901

T3 - International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM

SP - 555

EP - 560

BT - 2021 IEEE 22nd International Conference of Young Professionals in Electron Devices and Materials, EDM 2021 - Proceedings

PB - IEEE Computer Society

T2 - 22nd IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2021

Y2 - 30 June 2021 through 4 July 2021

ER -