Schmalhofer F J, Tschaitschian B
German Research Center for Artificial Intelligence, Kaiserslautern, Germany.
Methods Inf Med. 1998 Nov;37(4-5):491-500.
In this paper, we perform a cognitive analysis of knowledge discovery processes. As a result of this analysis, the construction-integration theory is proposed as a general framework for developing cooperative knowledge evolution systems. We thus suggest that for the acquisition of new domain knowledge in medicine, one should first construct pluralistic views on a given topic which may contain inconsistencies as well as redundancies. Only thereafter does this knowledge become consolidated into a situation-specific circumscription and the early inconsistencies become eliminated. As a proof for the viability of such knowledge acquisition processes in medicine, we present the IDEAS system, which can be used for the intelligent documentation of adverse events in clinical studies. This system provides a better documentation of the side-effects of medical drugs. Thereby, knowledge evolution occurs by achieving consistent explanations in increasingly larger contexts (i.e., more cases and more pharmaceutical substrates). Finally, it is shown how prototypes, model-based approaches and cooperative knowledge evolution systems can be distinguished as different classes of knowledge-based systems.
在本文中,我们对知识发现过程进行了认知分析。作为该分析的结果,提出了建构 - 整合理论,作为开发协作式知识进化系统的通用框架。因此,我们建议,对于医学新领域知识的获取,首先应该针对给定主题构建多元观点,这些观点可能包含不一致性和冗余性。只有在此之后,这些知识才会被整合到特定情境的限定中,早期的不一致性才会被消除。作为医学中此类知识获取过程可行性的证明,我们展示了IDEAS系统,该系统可用于临床研究中不良事件的智能记录。该系统能更好地记录药物的副作用。从而,通过在越来越大的背景(即更多病例和更多药物底物)中实现一致的解释,知识得以进化。最后,展示了原型、基于模型的方法和协作式知识进化系统如何作为不同类别的基于知识的系统被区分开来。