Abstract
IntroductionWhile 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.MethodsWe 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.ResultsThe 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.ConclusionWe 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.
Highlights
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 developed a first prototype called Doknosis, an interactive differential diagnosis application to parse and visualize the feedback that can be generated using the current state of the knowledge bases (KB)
In this article, we discuss the construction and preliminary testing of an open access medical knowledge base intended to spur the development of digital medical cognitive assistants
Summary
While early diagnostic decision support systems were built around knowledge bases, more recent systems employ machine learning to consume large amounts of health data. Müller et al BMC Medical Informatics and Decision Making (2019) 19:93 approaches for new DDSS which combine their strengths. The envisioned decision support systems would integrate ML-based AI, structured knowledge-based algorithms and heuristics similar to the dual system theory of human cognition [12] which distinguishes fast and non-conscious thinking (System 1) and analytical, slow and conscious (System 2) thinking. Knowledge-based systems [2,3,4] were inspired by the diagnostic methods taught in medical school, (e.g. Bayesian reasoning). The underlying knowledge base stores medical information in a structured manner so that a computer can automatically recommend diagnoses and a human can understand the differences in these choices. ML and knowledge-based systems have their strengths and weaknesses but likely perform best in combination
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
