Development of automated methods for reducing the risk of critical conditions, based on the analysis of medical records
This paper describes the methods of development of ontologies and ontological modes in medicine. We present four-level structure of knowledge representation. Using the basics of ontological methods of presenting knowledge, we developed algorithms to prevent risks of critical conditions and complications. The work is based on the model-theoretic approach to represent medical knowledge, which is shown through partial atomic diagrams of algebraic systems and of a patients’ cases data via Boolean-valued models. This data helped to develop ontology and ontological models of the «spinal deformity and spinal degenerative disease». The ontology model contains: a) general knowledge that is applicable for all patients, b) data on specific patients, and c) estimated knowledge that help doctors make recommendations. Estimated knowledge is a set of hypothetical possibilities that could lead to a patient’s critical condition or complications. We also developed an algorithm generating the estimated (fuzzy) knowledge based on the analysis of medical records. A software system generating recommendations to help prevent and reduce the risk of a patient’s critical condition (life threatening) was implemented. The results used in the study are from data of patients with spinal deformity or spinal degenerative diseases.
About Authors (Correspondence):
Naydanov Ch.A. – postgraduate student, e-mail: email@example.com
Palchunov D.E. – doctor of physical mathematical sciences, leading researcher, e-mail: firstname.lastname@example.org
Sazonova P.A. – master student, e-mail: email@example.com