The advantages of BMJ Clinical Intelligence compared to rule-based approaches In clinical medicine, we have had decades of experience with rule-based approaches. Many institutions and vendors have delivered those kinds of resources and tools. But they are difficult to manage at scale. Organizations need dozens of people to be involved in maintaining large bodies of rules and when you get into hundreds of rules, it becomes difficult to test and determine if they are all working appropriately in synchrony. The knowledge graph approach as opposed to the simple rule-based approach allows us to create a single model for a whole domain of practice be it in clinical medicine or population health. Knowledge graphs allow us to reason in different ways as compared to simple rule-based approaches. A knowledge graph allows us to use semantic reasoning and look across the graph for relationships between diseases and other diseases, between diseases and findings, between findings and other findings, between findings and drugs, and even between drugs and diseases. This knowledge graph approach now undergirds most of the modern big tech stacks around the world. It is behind some of the best mapping systems, social media, and search and retrieval systems. This approach is massively scalable and it is exciting to consider how knowledge graphs may work with, leverage, or complement large language models to define relationships, extract features, and make associations. These will all result in deeper insights that will benefit both patients and physicians. Knowledge graphs also give us the ability to project the entire patient data set against the graph and to get a better picture of the patient – a more comprehensive picture than what might emerge from using forward and backward chaining through rule bases. When we see the whole patient state reflected against the graph, it can tell us for example that we expected to know a certain fact about the patient but we do not see it or we do not see that a relevant procedure or investigation has been considered – as it is not seen in the data. This is a patient-centered, data-driven approach – projecting the data against the knowledge graph and getting a snapshot of the patient’s current state, and telling the end user who may or may not be familiar with that patient what are reasonable expectations for their care delivery or care journey. Another feature of knowledge graphs is their ability to handle much more complexity than simple rule-based approaches. Rules tend to be relatively condition-specific and so it is difficult to express the complexity of multimorbidity with rule-based systems. So knowledge graphs allow us to see a more comprehensive picture not only of the care domain but of the patient state that is reflected in the care domain.
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Why the BMJ Knowledge Graph
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