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

EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning