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Re-Structuring and Specific Similarity Computation of Electronic Medical Records

Electronic medical records (EMRs) have high value for research, as they contain the patient's personal information, medical history, clinical examination, treatment process, and other information. Analysis based on EMRs can effectively assist doctors in clinical decision-making, provide data support for clinical research as well as personalized healthcare service for patients. We introduce a novel approach for EMR similarity computation by re-structuring and filtering some parts of physical examination result. Our approach is motivated by observations that it is easier to distinguish disease bias special part than bias the whole EMR which maybe contain some ineffective information. Assuming the check parts are independent, we split them and select effective parts. Then, we apply Deep NLP, converting the word to vectors which can be used to measure syntactic and semantic word similarities better. In addition, We replace traditional Euclidean distance with Word Mover's Distance(WMD), a novel distance function between text documents. Finally, KNN cluster is been used to evaluate the similarity between EMRs. Compared with traditional method such as LDA and LSI, our proposed method achieved higher recall value of disease classification problem.