Cardiovascular DiseasesNCT04849195
Comparison of Different Feature Engineering Methods for Automated ICD Coding
An observational study of Cardiovascular Diseases, run by China National Center for Cardiovascular Diseases at 1 site in China.
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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Cardiovascular Diseases
- How many people
- 6,947 participants
- Where
- 1 siteChina
- Study dates
- 1 Mar 2021to Apr 2021
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Admissions in Fuwai Hospital
Who may be able to join
- Admissions in Fuwai Hospital, from January 1, 2019, to February 28, 2019
Who cannot join
- -
Trial locations and contacts
1 site in 1 country.
About this study
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.
Read the detailed 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.
- Conditions
- Cardiovascular Diseases
- Being tested
- No intervention
- Medical subject headings
- Cardiovascular Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No interventionOther
No intervention
| Study group | Role | What they receive |
|---|---|---|
| Model training and test group | Data set will be split into training group and test group, where training group will be used for model building, and test group for subsequent evaluation and verification. Receives: Other: No intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: At the end of enrollment
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Apr 2021
- Study start1 Mar 2021
- Primary completionApr 2021 (estimated)
- Study completionApr 2021 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Cardiovascular DiseasesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 6,947 participants (estimated)
Sponsor and investigators
- Lead sponsor
- China National Center for Cardiovascular Diseases
- Sponsor type
- Other government
- Responsible party
- Sponsor
- Investigators
- Wei Zhao, PhD, Principal investigator, China National Center for Cardiovascular Diseases
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04849195
- Sponsor's protocol ID
- 2021-1425-02
- Status last verified
- Apr 2021
- First submitted
- 13 Apr 2021
- Last updated
- 19 Apr 2021
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 19 Apr 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Comparison of Different Feature Engineering Methods for Automated ICD Coding
- ClinicalTrials.gov ID
- NCT04849195
- Acronym / sponsor ID
- 2021-1425-02
- Conditions
- Cardiovascular Diseases
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 6,947 participants (estimated)
- Lead sponsor
- China National Center for Cardiovascular DiseasesOther government
- Study start
- 1 Mar 2021 (actual)
- Primary completion
- Apr 2021 (estimated)
- Study completion
- Apr 2021 (estimated)
- First posted
- 19 Apr 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed 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.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Model training and test group | – | Other: No interventionData set will be split into training group and test group, where training group will be used for model building, and test group for subsequent evaluation and verification. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Admissions in Fuwai Hospital
Key inclusion criteria (1 listed)
- Admissions in Fuwai Hospital, from January 1, 2019, to February 28, 2019
Key exclusion criteria (1 listed)
- -
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No interventionNo intervention | Other | Model training and test group |
Primary outcome
ICD-10 codes for each admission
Time frame: At the end of enrollment
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.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 Apr 2021.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Wei Zhao, PhD principal investigator
China National Center for Cardiovascular Diseases
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Cardiovascular Diseases, run by China National Center for Cardiovascular Diseases at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 19 Apr 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
ICD-10 codes for each admission (time frame: At the end of enrollment). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 19 Apr 2021; AzTrials last refreshed its copy on 27 Oct 2026.