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

• Published in: PloS one Vol. 12; no. 3; p. e0173410
• Main Authors: Chen, YunZhi, Lu, HuiJuan, Li, LanJuan
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 17.03.2017; Public Library of Science (PLoS)
• Subjects: Algorithms; Artificial intelligence; Automation; Biology and Life Sciences; Classification; Codes; Computational biology; Computer and Information Sciences; Computer engineering; Computer science; Disease; Evaluation; Health informatics; Hepatitis; Humans; Information retrieval; Information technology; Innovations; International Classification of Diseases; Language; Linguistics; Mapping; Medical case management; Medical records; Medicine; Medicine and health sciences; Ontology; People and Places; Physical Sciences; Practice guidelines (Medicine); Query expansion; Research and Analysis Methods; Semantics; Similarity; Social Sciences; Strings; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0173410

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.