We built and successfully evaluated an open access knowledge base to facilitate the development of new medical diagnostic assistants. While we identified shortcomings during development and evaluation, we found the knowledge base to be a promising platform for decision support systems. Our prototype, as well as DXplain, showed the correct answer as “best suggestion” in 33% of the cases. Using 117 medical cases, we found the accuracy of the knowledge base and test algorithm to be comparable to established diagnostic tools such as Isabel and DXplain. The knowledge base contains over 2000 ICD-10 coded diseases and 450 RX-Norm coded medications, with over 8000 unique observations encoded as SNOMED or LOINC semantic terms. Testing was based on case reports from selected literature and medical student preparatory material. We developed a modified set-covering algorithm to benchmark the performance of our knowledge base compared to existing platforms.
We constructed an open access knowledge base and evaluated its potential in the context of a prototype decision support system.
We argue curated knowledge bases will remain an important component of future diagnostic decision support systems by providing ground truth and facilitating explainable human-computer interaction, but that prototype development is hampered by the lack of freely available computable knowledge bases. While early diagnostic decision support systems were built around knowledge bases, more recent systems employ machine learning to consume large amounts of health data.
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