Learning and inference in knowledge-based probabilistic model for
  medical diagnosis
  Based on a weighted knowledge graph to represent first-order knowledge and
combining it with a probabilistic model, we propose a methodology for the
creation of a medical knowledge network (MKN) in medical diagnosis. When a set
of symptoms is activated for a specific patient, we can generate a ground
medical knowledge network composed of symptom nodes and potential disease
nodes. By Incorporating a Boltzmann machine into the potential function of a
Markov network, we investigated the joint probability distribution of the MKN.
In order to deal with numerical symptoms, a multivariate inference model is
presented that uses conditional probability. In addition, the weights for the
knowledge graph were efficiently learned from manually annotated Chinese
Electronic Medical Records (CEMRs). In our experiments, we found numerically
that the optimum choice of the quality of disease node and the expression of
symptom variable can improve the effectiveness of medical diagnosis. Our
experimental results comparing a Markov logic network and the logistic
regression algorithm on an actual CEMR database indicate that our method holds
promise and that MKN can facilitate studies of intelligent diagnosis.
