Accessing structured health information through English queries and automatic deduction

While much health data is available online, patients who are not technically astute may be unable to access it because they may not know the relevant resources, they may be reluctant to confront an unfamiliar interface, and they may not know how to compose an answer from information provided by multiple heterogeneous resources. We describe ongoing research in using natural English text queries and automated deduction to obtain answers based on multiple structured data sources in a specific subject domain. Each English query is transformed using natural language technology into an unambiguous logical form; this is submitted to a theorem prover that operates over an axiomatic theory of the subject domain. Symbols in the theory are linked to relations in external databases known to the system. An answer is obtained from the proof, along with an English language explanation of how the answer was obtained. Answers need not be present explicitly in any of the databases, but rather may be deduced or computed from the information they provide. Although English is highly ambiguous, the natural language technology is informed by subject domain knowledge, so that readings of the query that are syntactically plausible but semantically impossible are discarded. When a question is still ambiguous, the system can interrogate the patient to determine what meaning was intended. Additional queries can clarify earlier ones or ask questions referring to previously computed answers. We describe a prototype system, Quadri, which answers questions about HIV treatment using the Stanford HIV Drug Resistance Database and other resources. Natural language processing is provided by PARCs Bridge, and the deductive mechanism is SRIs SNARK theorem prover. We discuss some of the problems that must be faced to make this approach work, and some of our solutions.

Citation

Waldinger, R. J.; Bobrow, D. G.; Condoravdi, C.; Richardson, K.; Das, A. Accessing structured health information through English queries and automatic deduction. AI and Health Communication; Papers from the 2011 AAAI Spring Symposium; 2011 March 21-23; Stanford, CA; AAAI Technical Report SS-11-01. Menlo Park, CA: AAAI Press; 2011.