Comparison of Different Feature Engineering Methods for Automated ICD Coding

Cardiovascular Diseases Clinical Trial

Official title:

Comparison of Different Feature Engineering Methods for Automated ICD Coding

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT04849195
• Other study ID #: 2021-1425-02
• Status: Active, not recruiting
• Start date: March 1, 2021
• Est. completion date: April 2021

Study information

• Verified date: April 2021
• Source: China National Center for Cardiovascular Diseases
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Using traditional machine learning classifiers, this study targets on comparing bag-of-words, word2cec and roberta on automated ICD coding related to cardiovascular diseases in Chinese corpus.

Description:

ICD coding is quite important as it serves as basis for a wide range of economic and academic applications. Currently, manual coding is mainly adopted, which faces several limits like being time-consuming and prone to error, and this makes automated ICD coding via machine learning a hot research topic. As an inevitable phase during machine learning, feature engineering plays a crucially important role in leading to promising coding performance. Although have reached enlightening conclusions, existing studies lacked comparison of different feature engineering methods. Finding out what methods under what circumstances perform better can be quite helpful in promoting practical applications of automated coding. The investigators will implement this study based on inpatient' data collected from electronic medical records from Fuwai Hospital, the world's largest medical center for cardiovascular disease. Bag-of-words, word2cec and roberta will be respectively used to extracted features from training data. Then code-wise logistic regression classifiers and support vector machine classifiers will be trained to auto-assign codes. Afterwards, performances of the models on test data will be evaluated.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 6947
• Est. completion date: April 2021
• Est. primary completion date: April 2021
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A and older

Eligibility

Inclusion Criteria:

• Admissions in Fuwai Hospital, from January 1, 2019, to February 28, 2019 Exclusion Criteria: -

Related Conditions & MeSH terms

• Cardiovascular Diseases

Intervention

Other:
• No intervention
No intervention

Locations

Country	Name	City	State
China	Fuwai Hospital	Beijing	Beijing

Sponsors (1)

Lead Sponsor	Collaborator
China National Center for Cardiovascular Diseases

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	ICD-10 codes for each admission	Each admission will be a sample in this study. The ICD-10 codes assigned by medical coders for each admission will be collected as the primary outcome.	At the end of enrollment