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. Electronic supplementary material The online version of this article (10.1186/s12911-019-0804-1) contains supplementary material, which is available to authorized users.

Effects of a Differential Diagnosis List of Artificial Intelligence on Differential Diagnoses by Physicians: An Exploratory Analysis of Data from a Randomized Controlled Study

“…A diagnostic decision support system (DDSS) that supports the diagnostic process by generating differential diagnoses from observations provided [ 3 ] is expected to reduce diagnostic errors by reducing cognitive biases and reinforcing physicians’ knowledge. It is said that generating potential diagnoses is a rate-limiting step of differential diagnosis for novice diagnosticians [ 2 ], and their diagnostic accuracy was reported to be associated with the number of generated diagnoses [ 4 ].…”

Pragmatic Considerations on Clinical Decision Support from the 2019 Literature

“…Canovas et al, [10] formalize EUCAST expert rules as an ontology and production rules to detect antimicrobial therapies at risk of failure. Müller et al, [11] propose an open diagnostic knowledge base that can compete with commercial ones. Replacing humans is another topic of research and Spänig et al, [12] work on two aspects to virtualize a doctor: the automatic acquisition of data through sensors and speech recognition, and the automation of diagnostic reasoning.…”

Ontology of anomalous processes diagnosis

“…As a rule, in known ontologies and in diagnostic knowledge, the level of complexity of relationships is approximately as follows: “for a diagnosis mandatory criteria, the choice of two criteria among several diagnostic criteria, and numerical criteria are used” 23,24 …”

“…When adapting an ontology (from universal, problem-oriented to concretized in a domain) we have the opportunity to offer experts a more appropriate terminology, in particular, in medicine to "cover" the clinical picture and the development process not only the symptoms fixed at the doctor's appointment, as in most DSS, 9,20,23,24 but alsophysical examination and relevant investigations.…”

“…As a rule, in known ontologies and in diagnostic knowledge, the level of complexity of relationships is approximately as follows: "for a diagnosis mandatory criteria, the choice of two criteria among several diagnostic criteria, and numerical criteria are used". 23,24 days after the event (impact), such as: "a trip to high-risk region", for example, "the city Wuhan", "contact with a person infected with COVID-2019", visiting a medical facility, contacting someone who has visited high-risk areas, visiting an airport. The overload of the AUUV engine can be caused by boundary modes of movement, characterized by increased load on the screw or jamming of the engine shaft due to the fact that fish, fishing net or grass got in.…”