PLoS ONE · 2017 · 63 citations · 20 references
ComputingBiomedical InformaticsComputer ScienceBiomedical Text MiningMedical CodingNatural Language ProcessingClinical Natural Language ProcessingCode MappingElectronic Health Record CodingSequence Similarity SearchString AlgorithmsTerminology MappingDisease Entity RecognitionDisease Name RecognitionIcd CodingInternational Classification Of DiseasesLongest Common SubsequenceLongest Common SubstringSemantic Similarity
ICD-10(International Classification of Diseases 10th revision) is a classification of a disease, symptom, procedure, or injury. Diseases are often described in patients' medical records with free texts, such as terms, phrases and paraphrases, which differ significantly from those used in ICD-10 classification. This paper presents an improved approach based on the Longest Common Subsequence (LCS) and semantic similarity for automatic Chinese diagnoses, mapping from the disease names given by clinician to the disease names in ICD-10. LCS refers to the longest string that is a subsequence of every member of a given set of strings. The proposed method of improved LCS in this paper can increase the accuracy of processing in Chinese disease mapping.
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