Interesting things for computer systems to do: Keeping and data mining millions of patient records, guiding patients and physicians, and passing medical licensing exams

摘要

The extraction of medical knowledge from data mining many patient records and from authoritative natural language text on the Internet is important for clinical decision support. Here is discussed how such knowledge expressed in our Q-UEL language as semantic triples analogous to subject-verb-object, and now more elaborate semantic multiples, can respond to examination-style medical questions in natural language. Importantly, it is also discussed how our inference methods can be used to assign a probability to each answer in the set of candidate answers. We had intended to “show off” the power of our Q-UEL language by drawing on its many algorithmic and knowledge archive resources. However, in view of the simplicity of the approach used here, originally only intended to set prior probabilities, it is interesting that it is often alone sufficient to give the right answer, giving performance comparable to that of a typical medical student. Nonetheless, it still needs a large knowledge representation store, and Q-UEL has several tools to extract and integrate knowledge from (a) structured data, (b) unstructured data, and (c) by dialogue with human experts.