Comparison of Different Feature Engineering Methods for Automated ICD Coding
Study Details
Study Description
Brief 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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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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
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| 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. |
Other: No intervention
No intervention
|
Outcome Measures
Primary Outcome Measures
- ICD-10 codes for each admission [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.
Eligibility Criteria
Criteria
Inclusion Criteria:
- Admissions in Fuwai Hospital, from January 1, 2019, to February 28, 2019
Exclusion Criteria:
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Fuwai Hospital | Beijing | Beijing | China | 100037 |
Sponsors and Collaborators
- China National Center for Cardiovascular Diseases
Investigators
- Principal Investigator: Wei Zhao, PhD, China National Center for Cardiovascular Diseases
Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- 2021-1425-02