An Approach to Automated Extraction of Diagnostic Rules From the Text of Clinical Guidelines for Decision Support Systems
- DOI
- 10.2991/aisr.k.201029.003How to use a DOI?
- Keywords
- decision support system, medicine, clinical guidelines, natural language processing, syntactic analysis, dependency grammar, phrase structure grammar
- Abstract
Currently, there is a large amount of accumulated medical knowledge about various diseases, formalized in the form of clinical guidelines. For general practitioners, it is difficult to remember several dozen documents, due to information overloaded. To solve this problem, medical decision support systems (DSS) are used. Those DSS based on digitized clinical guidelines, which are used to help doctors make more accurate diagnoses and prescribe treatment to patients. Periodic updating of clinical practice guidelines raises an additional problem with timely updates to the rules in the DSS. Natural language analysis methods were used to extract information from clinical guidelines. As a result of the analysis of natural language analysis methods, the dependency grammar method was chosen as the most suitable for the Russian language. In conclusion we has been built a prototype of a program for extracting information from the text of clinical guidelines. This program allows extracting of “if-then” rules in the “treatment” Chapter, whose performance has been tested on clinical guidelines for various diseases.
- Copyright
- © 2020, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Ruslan Vafin AU - Rashit Nasyrov AU - Rustem Zulkarneev PY - 2020 DA - 2020/11/10 TI - An Approach to Automated Extraction of Diagnostic Rules From the Text of Clinical Guidelines for Decision Support Systems BT - Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020) PB - Atlantis Press SP - 12 EP - 18 SN - 1951-6851 UR - https://doi.org/10.2991/aisr.k.201029.003 DO - 10.2991/aisr.k.201029.003 ID - Vafin2020 ER -