Cardiovascular Diseases
Conditions
Brief summary
This study aims to develop and validate machine learning model in ICD-10 coding of primary diagnosis related to cardiovascular diseases in Chinese corpus.
Detailed description
The accuracy and productivity of ICD coding has always been a concern of clinical practice. Errors of ICD codes may result in claim denials and missed revenue. However, ICD coding process is complex, time-consuming and error-prone. More experienced coders are in need, but there is an increasing lack of supply. Automated ICD coding has potential to facilitate clinical coders for improved efficiency and quality. Model performance of related studies is still far below coders and both the accuracy and interpretability need to be improved in great demand. Besides, studies in Chinese corpus are not sufficient. In this study, the investigators will implement automated ICD coding study based on inpatient' data collected from electronic medical records from Fuwai Hospital, the world's largest medical center for cardiovascular disease. Feature engineering and machine learning methods will be used to develop classification models with good performance, interpretability and practicability for ICD codes of primary diagnosis.
Interventions
OTHERNo intervention
No intervention
Sponsors
China National Center for Cardiovascular Diseases
Study design
Observational model
OTHER
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
Inclusion criteria
* Admissions in Fuwai Hospital, from January 1, 2019, to December 31, 2020
Exclusion criteria
* Admissions stayed in nephrology department, Fuwai Hospital
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| ICD code of primary diagnosis | At the end of enrollment | Each admission will be a sample in this study. The ICD code of primary diagnosis assigned by medical coders for each admission will be collected as the primary outcome. |
Countries
China
Outcome results
None listed