Evidence map›Paper›PMID 28306739›Full record

ArticlePloS one2017

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

YunZhi Chen, HuiJuan Lu, LanJuan Li

Abstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Construction of a semi-automatic ICD-10 coding system.BMC medical informatics and decision making · 2020
    Article
  8. Explainable Prediction of Medical Codes With Knowledge Graphs.Frontiers in bioengineering and biotechnology · 2020
    Article
  9. Article
  10. Article
  11. EHR problem list clustering for improved topic-space navigation.BMC medical informatics and decision making · 2019
    Article
  12. Automatic ICD Code Assignment based on ICD's Hierarchy Structure for Chinese Electronic Medical Records.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019
    Article
  13. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

YunZhi ChenZhejiang University School of Medicine, Hangzhou, China.ORCID http://orcid.org/0000-0001-5917-3836
HuiJuan LuCollege of Information Engineering of China Jiliang University, Hangzhou, China.
LanJuan LiZhejiang University School of Medicine, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AlgorithmsInternational Classification of DiseasesHumans

Identifiers

PMID28306739
PMCPMC5356997

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.