EMR-based medical knowledge representation and inference via Markov
  random fields and distributed representation learning
  Objective: Electronic medical records (EMRs) contain an amount of medical
knowledge which can be used for clinical decision support (CDS). Our objective
is a general system that can extract and represent these knowledge contained in
EMRs to support three CDS tasks: test recommendation, initial diagnosis, and
treatment plan recommendation, with the given condition of one patient.
Methods: We extracted four kinds of medical entities from records and
constructed an EMR-based medical knowledge network (EMKN), in which nodes are
entities and edges reflect their co-occurrence in a single record. Three
bipartite subgraphs (bi-graphs) were extracted from the EMKN to support each
task. One part of the bi-graph was the given condition (e.g., symptoms), and
the other was the condition to be inferred (e.g., diseases). Each bi-graph was
regarded as a Markov random field to support the inference. Three lazy energy
functions and one parameter-based energy function were proposed, as well as two
knowledge representation learning-based energy functions, which can provide a
distributed representation of medical entities. Three measures were utilized
for performance evaluation. Results: On the initial diagnosis task, 80.11% of
the test records identified at least one correct disease from top 10
candidates. Test and treatment recommendation results were 87.88% and 92.55%,
respectively. These results altogether indicate that the proposed system
outperformed the baseline methods. The distributed representation of medical
entities does reflect similarity relationships in regards to knowledge level.
Conclusion: Combining EMKN and MRF is an effective approach for general medical
knowledge representation and inference. Different tasks, however, require
designing their energy functions individually.
