Emerging medical informatics with case-based reasoning for aiding clinical decision in multi-agent system

Abstract

This research aims to depict the methodological steps and tools about the combined operation of case-based reasoning (CBR) and multi-agent system (MAS) to expose the ontological application in the field of clinical decision support. The multi-agent architecture works for the consideration of the whole cycle of clinical decision-making adaptable to many medical aspects such as the diagnosis, prognosis, treatment, therapeutic monitoring of gastric cancer. In the multi-agent architecture, the ontological agent type employs the domain knowledge to ease the extraction of similar clinical cases and provide treatment suggestions to patients and physicians. Ontological agent is used for the extension of domain hierarchy and the interpretation of input requests. Case-based reasoning memorizes and restores experience data for solving similar problems, with the help of matching approach and defined interfaces of ontologies. A typical case is developed to illustrate the implementation of the knowledge acquisition and restitution of medical experts.

Case Based Reasoning for Green Information Systems Infusion and Assimilation among IT Professionals in University Campuses

“…In addition, the exceptional feature of CBR is that it does not need to match the query of IT professionals precisely, e.g., in searching problems [9]. The simple theory of CBR is that similar problems have similar solutions, even if the case base does not contain a solution that directly resolves the problem of the IT professional [9]. A solution can be obtainable for use as a starting point.…”

“…The phases comprise: (a) retrieving, in which an assumed speci c problem retrieves cases similar to the case base to solve the new problem [7]; (b) reusing, which involves selecting the possible solutions from the retrieved cases [8]. If the solutions are not suitable to use straight away, they may be adapted to the new situation; (c) revising, which comprises changing existing solution to the target problem [9]. It also involves testing the new solution to the problem and, if needed, it continues to revise [10]; (d) retaining, which stores the resultant new cases in the case base if the solution has e ectively been applied to the target problem [8].…”

Constructing Ontology-Based Cancer Treatment Decision Support System with Case-Based Reasoning

“…Based on our previous work [3], Fig. 2 presents the relations between diagnosis (Δ), prognosis (Π), and treatment (Θ) and describes a series of medical inference rules using a simulated treatment approach.…”

Forecasting of Industrial Water Demand Using Case-Based Reasoning—A Case Study in Zhangye City, China

“…The basic assumptions of CBR are that similar problems have similar solutions [29]. The paradigm of CBR processing problems is inspired by the human decision-making process [43]. It collects a set of cases, …”