Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
The Use of Model-Theoretical Methods for Automated Knowledge Extraction from Medical Texts. / Pogodin, Ruslan S.; Palchunov, Dmitry.
2021 IEEE 22nd International Conference of Young Professionals in Electron Devices and Materials, EDM 2021 - Proceedings. IEEE Computer Society, 2021. p. 555-560 9507606 (International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM; Vol. 2021-June).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
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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 -
ID: 34163236