Leveraging XML-based electronic medical records to extract experiential clinical knowledge. An automated approach to generate cases for medical case-based reasoning systems.
Case-based reasoning (CBR)-driven medical diagnostic systems demand a critical mass of up-to-date diagnostic-quality cases that depict the problem-solving methodology of medical experts. In practical terms, procurement of CBR-compliant cases is quite challenging, as this requires medical experts to map their experiential knowledge to an unfamiliar computational formalism. In this paper, we propose a novel medical knowledge acquisition approach that leverages routinely generated electronic medical records (EMRs) as an alternate source for CBR-compliant cases. We present a methodology to autonomously transform XML-based EMR to specialized CBR-compliant cases for CBR-driven medical diagnostic systems. Our multi-stage methodology features: (a) collection of heterogeneous EMR from Internet-accessible EMR repositories via intelligent agents, (b) automated transformation of both the structure and content of generic EMR to specialized CBR-compliant cases, and (c) inductive estimation of the weight of each case-defining attribute. The computational implementation of our methodology is presented as case acquisition and transcription info-structure (CATI).
Abidi
Syed Sibte Raza
SS
Faculty of Computer Science, Dalhousie University, 6050 University Avenue, NS, B3S 1J3, Halifax, Canada. sraza@cs.dal.ca
Manickam
Selvakumar
S
D001185:Artificial Intelligence; D006801:Humans; D008128:Logic; D008492:Medical Informatics Computing; D016347:Medical Records Systems, Computerized; D011340:Problem Solving; D013358:Subject Headings; D017432:Unified Medical Language System; D018875:Vocabulary, Controlled