An open access medical knowledge base for community driven diagnostic decision support system development

Abstract

Introduction: While early diagnostic decision support systems were built around knowledge bases, more recent systems employ machine learning to consume large amounts of health data. 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.

Methods: We constructed an open access knowledge base and evaluated its potential in the context of a prototype decision support system. We developed a modified set-covering algorithm to benchmark the performance of our knowledge base compared to existing platforms. Testing was based on case reports from selected literature and medical student preparatory material.

Results: 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. 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. Our prototype, as well as DXplain, showed the correct answer as "best suggestion" in 33% of the cases. While we identified shortcomings during development and evaluation, we found the knowledge base to be a promising platform for decision support systems.

Conclusion: We built and successfully evaluated an open access knowledge base to facilitate the development of new medical diagnostic assistants. This knowledge base can be expanded and curated by users and serve as a starting point to facilitate new technology development and system improvement in many contexts.

Keywords: Decision support systems, clinical (D020000); Diagnosis, differential (D003937); Knowledge bases (D051188).

Fig. 1

The majority of diseases is described by less then 10 symptoms, but there is a long tail to up to 67 symptoms for single disease

Fig. 2

Screenshots of the Doknosis user interface depicting a typical use case and the top 10 explanations for two different options. Up to 20 results can be displayed and are ranked according to their calculated score which grows with the number of related observations. Subfigure (a) shows the query interface with the symptoms for an ebola patient, (b) shows the resulting list if only North America is selected, and (c) depicts the results if Africa is included