2016
DOI: 10.1186/s12911-016-0269-4
|View full text |Cite
|
Sign up to set email alerts
|

A hierarchical method to automatically encode Chinese diagnoses through semantic similarity estimation

Abstract: BackgroundThe accumulation of medical documents in China has rapidly increased in the past years. We focus on developing a method that automatically performs ICD-10 code assignment to Chinese diagnoses from the electronic medical records to support the medical coding process in Chinese hospitals.MethodsWe propose two encoding methods: one that directly determines the desired code (flat method), and one that hierarchically determines the most suitable code until the desired code is obtained (hierarchical method… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
24
0

Year Published

2017
2017
2024
2024

Publication Types

Select...
6
3
1

Relationship

0
10

Authors

Journals

citations
Cited by 44 publications
(24 citation statements)
references
References 28 publications
0
24
0
Order By: Relevance
“…Another way to deal with lexical variability is to work directly with meanings. In this line, Chen et al have explored the Longest Common Subsequence (LCS) of concepts as a feature for the classification [10], and Ning et al have exploited the hierarchical structure using a distributional 2 https://www.asco.org/practice-guidelines/ billing-coding-reporting/icd-10/ general-equivalence-mappings-gems semantic [11]. Other approaches have applied neural networks fed with word embeddings trained on external corpora [12], [13].…”
Section: A Icd-10 Codingmentioning
confidence: 99%
“…Another way to deal with lexical variability is to work directly with meanings. In this line, Chen et al have explored the Longest Common Subsequence (LCS) of concepts as a feature for the classification [10], and Ning et al have exploited the hierarchical structure using a distributional 2 https://www.asco.org/practice-guidelines/ billing-coding-reporting/icd-10/ general-equivalence-mappings-gems semantic [11]. Other approaches have applied neural networks fed with word embeddings trained on external corpora [12], [13].…”
Section: A Icd-10 Codingmentioning
confidence: 99%
“…Several studies adopt model architectures which reflect this structure. One approach trains a binary SVM for each node in an ontology, with each classifier learning only from training examples classed as positive by its parent classifier [34,35,36,37,38]. A framework has been described for feedforward neural network training which is regularised so as to incorporate tree-based priors derived from disease ontologies [39].…”
Section: Related Workmentioning
confidence: 99%
“…However, coders may code each disease separately. Third, ICD-10 codes are organized in a hierarchical structure where the top-level codes represent generic disease categories and the bottom-level codes represent more specific diseases [24], and the coder may match the diagnosis description to a generic code instead of a specific code. In addition, the coders could make obvious errors due to carelessness when the workload is heavy.…”
Section: Introductionmentioning
confidence: 99%