Strategic Health Information Management and Forecast: The Birdwatching Approach

Abstract

Abstract.To facilitate communication and the exchange of information between patients, nurses, lab technicians, health insurers, physicians, policy makers, and existing knowledge-based systems, a set of shared standard terminologies and controlled vocabularies are necessary. In modern health information management systems, these vocabularies are defined within formal representations called ontologies, where terminologies are only meaningful once linked to a descriptive dataset. When the datasets and their conveyed knowledge are changed, the ontological structure is altered accordingly. Despite the importance of this topic, the problem of managing evolving ontological structures is inadequately addressed by available tools and algorithms, partly because handling ontological change is not a purely computational affair. In this paper, we propose a framework inspired by a social activity, birdwatching. Using this model, the evolving ontological structures can be monitored and analyzed based on their state at a given time. Moreover, patterns of changes can be derived and used to predict and approximate a system's behavior based on potential future changes.

An abstract representation model for evolutionary analysis of multi-agent interactions

“…The manual combination of rules is neither desirable nor feasible in many circumstances (e.g., when dealing with large sets of rules). The mathematical power of categories can deliver a formal guidance for combining these sets of rules, which are usually described in a diagrammatic representation 4 . For example, in the RLR framework, the agents follow certain rules, some simple and some complex ones (in the case of multiple options leading to different decision points, e.g., adding concepts, which needs the combination of several rules to find a place, check the validity, and so on).…”

“…In order to support and improve the level of automation for analyzing and managing evolving biomedical ontologies and knowledge bases, in our earlier works [3,4], we have proposed the RLR agent-based framework for Representation, Legitimation and Reproduction of changes in distributed ontologies with minimum human intervention. To support the negotiation, communication and interaction between different intelligent agents, in an automatic manner, a mathematical knowledge representation formalism is necessary.…”

“…As an example, following the approaches presented in [39] and [38], we may merge the two states St 3 and St 4 , which respectively represent the state of the RLR system after querying to determine the type of change and receiving the proposed answers, into one merged state St 3,4 (Figure 10 (a)). As another example, Figure 10 (b) demonstrates the transformation of a state diagram to the new diagram, representing the concurrencies between the two states St 5 (validation of the accepted response), and state St 6 (ask permission to put the results into an action).…”