Nanopublishing Clinical Diagnoses: Tracking Diagnostic Knowledge Base Content and Utilization

Accurate and evidence-based diagnosis is a key step in clinical practice. High-quality diagnoses depend on several factors, including physician's training and experience. To assist physicians, medical diagnosis systems can be used, as part of clinical decision support systems (CDSS), to improve the accuracy of diagnoses, as well as inform the clinician regarding the bases of the diagnostic decisions in the context of prior knowledge. To support such CDSS systems, it is important to have accurate and well-formed knowledge bases with thoroughly annotated diagnostic criteria, as well as models for representing clinical observations that allow them to more easily be analyzed by expert-systems. We propose the use of Nan publications as a way to store provenance data related to the content of diagnostic knowledge bases, as well as the clinical diagnoses themselves. The primary goal is to be able to rigorously track the complete diagnostic process: from the knowledge base construction and its supporting evidence, to the clinical observations and the context within which they were made, through to the diagnosis itself, and the rationale behind it.