
Automata approach for personalized support of clinical processes in healthcare
Author(s) -
Yurii Aksenov,
Nataliya Dobrenko,
Aleksandra Vatyan,
Roman V. Kapustin,
Svyatoslav Osipov,
Pavel Mavrin,
Nataliya Gusarova,
Анатолий Абрамович Шалыто
Publication year - 2019
Publication title -
informacionno-upravlâûŝie sistemy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.202
H-Index - 6
eISSN - 2541-8610
pISSN - 1684-8853
DOI - 10.31799/1684-8853-2019-5-64-75
Subject(s) - computer science , automaton , personalization , process (computing) , set (abstract data type) , relevance (law) , human–computer interaction , artificial intelligence , programming language , world wide web , political science , law
Within the framework of the National Healthcare Project, personalization of a physician’s activity is very important,forming a demand for a Clinical Decision Support System. The available systems miss the functions of prompting a doctor during theclinical process or identifying possible contradictions between different types of medical treatment offered to the patient. Purpose:Development of a solution, free from the above-mentioned problems, for personalized support of the clinical process. Methods:Automata (state machine) approach presenting the clinical process as a set of automata states and possible transitions between them,and a set of design patterns, namely: Abstract Factory, Facade, Adapter and Visitor. Results: A solution for personalized supportof clinical processes is proposed, based on the automata approach and design patterns. The automata approach allows you to dividethe clinical process into separate stages and automatically control the possible transitions and conditions for their implementation,including checking for c ontraindications. The use of design patterns provides a sufficient degree of generalization, allowing you,without affecting the structure of the main application code, to promptly connect the system to the necessary sources of information,and to enter the data about contradictions of various origins, taking them into account when making decisions on the treatment of aparticular patient. Practical relevance: The developed solution, as compared to the available systems, is more efficient at promptingthe doctor during a clinical process, and at identifying possible contradictions between the various types of medical treatment offeredto the patient.