Development and validation of method for defining conditions using Chinese electronic medical record
Bibliographic record
Abstract
BACKGROUND: The adoption of the electronic medical record (EMR) is rapidly growing in China. Constantly evolving, Chinese EMRs contain vast amounts of clinical and financial data, providing tremendous potential for research and policy use; however, they are only partially standardized and contain free text or unstructured data. To utilize the information contained in Chinese EMRs, the development of data extraction methodology is urgently needed. The purpose of this study is to develop and validate methods to extract clinical information from the Chinese EMR for research use. METHODS: Using 2010 to 2014 EMR data from YouAn Hospital, a large teaching hospital affiliated with Capital Medical University in Beijing, China, we developed extraction methods including 40 EMR definitions for defining 6 liver disease, 5 disease severity conditions, and 29 comorbidities and treatments. We conducted a chart review of 450 randomly selected EMRs. Using physician chart review results as a reference, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to validate each EMR definition. RESULTS: The sensitivity of the 6 EMR definitions for liver diseases ranged from 78.9 to 100.0 %, and PPV ranged from 82.1 to 100.0 %. The sensitivity of the 5 definitions on disease severity conditions ranged from 91.0 to 100.0 %, and PPV ranged from 79.2 to 100.0 %. Among the 29 EMR definitions for comorbidities and treatments, 23 had sensitivity over 90.0 % and 25 had PPV over 80.0 %. The specificity and NPV for all 40 EMR definitions were over 90.0 %. CONCLUSION: The extraction method developed is a valid way of extracting information on liver diseases, comorbidities and related treatments from YouAn hospital EMRs. Our method should be modified for application to other Chinese EMR systems, following our framework for extracting conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.055 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.018 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".