Abstract
The paper aims at two tasks of electronic medical record (EMR) processing: EMR retrieval and medical term extraction. The linguistic phenomena in EMRs in different departments are analyzed in depth including record size, vocabulary, entropy of medical languages, grammaticality, and so on. We explore various techniques of information retrieval for EMR retrieval, including five retrieval models with six pre-processing strategies on different parts of EMRs. The learning to rank algorithm is also adopted to improve the retrieval performance. Finally, our retrieval model is applied to extract medical terms from EMRs. Both coarse-grained relevance evaluation on department level and fine-grained relevance evaluation on treatment level are conducted.
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Huang, HH., Lee, CC., Chen, HH. (2014). Mining Professional Knowledge from Medical Records. In: Ślȩzak, D., Tan, AH., Peters, J.F., Schwabe, L. (eds) Brain Informatics and Health. BIH 2014. Lecture Notes in Computer Science(), vol 8609. Springer, Cham. https://doi.org/10.1007/978-3-319-09891-3_15
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DOI: https://doi.org/10.1007/978-3-319-09891-3_15
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