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Automatic ICD-10 coding algorithm using an improved longest common subsequence based on semantic 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 clas...

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Detalles Bibliográficos
Autores principales: Chen, YunZhi, Lu, HuiJuan, Li, LanJuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5356997/
https://www.ncbi.nlm.nih.gov/pubmed/28306739
http://dx.doi.org/10.1371/journal.pone.0173410
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author Chen, YunZhi
Lu, HuiJuan
Li, LanJuan
author_facet Chen, YunZhi
Lu, HuiJuan
Li, LanJuan
author_sort Chen, YunZhi
collection PubMed
description 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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spelling pubmed-53569972017-03-30 Automatic ICD-10 coding algorithm using an improved longest common subsequence based on semantic similarity Chen, YunZhi Lu, HuiJuan Li, LanJuan PLoS One Research Article 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. Public Library of Science 2017-03-17 /pmc/articles/PMC5356997/ /pubmed/28306739 http://dx.doi.org/10.1371/journal.pone.0173410 Text en © 2017 Chen et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Chen, YunZhi
Lu, HuiJuan
Li, LanJuan
Automatic ICD-10 coding algorithm using an improved longest common subsequence based on semantic similarity
title Automatic ICD-10 coding algorithm using an improved longest common subsequence based on semantic similarity
title_full Automatic ICD-10 coding algorithm using an improved longest common subsequence based on semantic similarity
title_fullStr Automatic ICD-10 coding algorithm using an improved longest common subsequence based on semantic similarity
title_full_unstemmed Automatic ICD-10 coding algorithm using an improved longest common subsequence based on semantic similarity
title_short Automatic ICD-10 coding algorithm using an improved longest common subsequence based on semantic similarity
title_sort automatic icd-10 coding algorithm using an improved longest common subsequence based on semantic similarity
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5356997/
https://www.ncbi.nlm.nih.gov/pubmed/28306739
http://dx.doi.org/10.1371/journal.pone.0173410
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