The Japanese public health system relies upon a mandatory insurance scheme that
subsidizes every medical procedure. This causes some practitioners in doubt to
order unnecessary exams, especially in departments like the emergency room (ER)
(where time and personnel constraints apply), generating additional costs for
the public health system. In this context arises the need and challenge of
developing a computer application based on Artificial Intelligence that, given
a patient's symptoms upon entering the ER, recommends the most appropriate
exams to increase the accuracy of the diagnosis. This paper presents the
preliminary results on the development of such a tool using a Bayesian Network
(BN). Although there is a lot of literature on BN for medical diagnosis, this
work is innovative as it is focused on suggesting useful exams based on
pre-test probabilities, and that it was built using only medical data and other
freely available information sources. A fundamental disease list was
established using a Human Symptom-Disease Network (HSDN) containing
symptom-disease relationships. The co-occurrence between disease and symptom
terms on the HSDN was translated into rough sensitivity and specificity
estimates and used to set the conditional probabilities of the BN. Prior
probabilities of diseases were estimated using hospital data of regular and
emergency visits. Information about findings (exams) and their
sensitivityspecificity data was scraped from web databases and mapped into the
network. Preliminary tests for inspecting the accuracy of the developed tool
were made with the help of a medical expert, based on relevant literature.
Obtained results show that the tool is able to find differential diagnoses for
most cases. This work opens the door for future improvements of the system.