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. 2017 Mar 17;12(3):e0173410.
doi: 10.1371/journal.pone.0173410. eCollection 2017.

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

YunZhi Chen  1   2 , HuiJuan Lu  3 , LanJuan Li  1   4
Affiliations

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

YunZhi Chen et al. PLoS One. .

Abstract

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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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Flowchart of LCS algorithm.
Fig 2
Fig 2. Corpus of Chinese word segmentation of 181 kinds of hepatitis.
Fig 3
Fig 3. Similarity line chart when L(A) = 1.
Fig 4
Fig 4. Similarity line chart when L(A) = 2.
Fig 5
Fig 5. Similarity line chart when L(A) = 3.
Fig 6
Fig 6. Similarity line chart when L(A) = 4.
Fig 7
Fig 7. Similarity line chart when L(A) = 5.
Fig 8
Fig 8. Similarity line chart when L(A) = 6.
Fig 9
Fig 9. Accuracy analysis chart under similarity threshold (n = 1000).
Fig 10
Fig 10. Given threshold of coding accuracy and percentage.
See this image and copyright information in PMC

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