Developing and Validating Electronic Medical Record Based Case Definitions for Liver Diseases and Comorbidities
Bibliographic record
Abstract
ABSTRACTObjectiveChina has collected high volume of electronic health record (EMR) data. The rich information in EMR data could be used for health services research. The first challenge is developing methods for extracting study variables. Our study aimed to develop and validate data extraction methods for defining clinical conditions. ApproachThe EMRs were from Beijing You-An Hospital, a leading liver diseases specialized teaching hospital affiliated with Capital Medical University in China. We developed EMR based case definitions for extracting common liver diseases and comorbidities in Charlson and Elixhauser comorbidity algorithms. We developed the EMR case definitions based on the fundamental EMR structure and clinical expertise. To determine validity of the EMR case definitions, we calculated the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for each case definition based on the “gold standard”: randomly selected 450 charts review conducted by two hepatologists. The agreement between the two reviewers was assessed by kappa value. ResultsIn total, 69,864 EMRs for adult patients with liver disease admitted between 2010 and 2015 were included in this study. Among these patients, we identified 13,763 (19.7%) PLC, 18,654 (26.7%) Hepatitis B Virus (HBV), 4681 (6.7%) Hepatitis C Virus (HCV), 18,933 (27.1%) cirrhosis, 7,964 (11.4%) diabetes, 8,523 (12.2%) hypertension, 3,283 (4.7%) Fluid and Electrolyte Disorder (FED), 3,563 (5.1%) renal disease and 1,258 (1.8%) Cerebrovascular Disease (CEVD). Between the two reviewers, the Kappa values fell between 0.65 and 1.00. Of the 450 EMRs reviewed, the two reviewers identified 95 (21.1%) PLC, 197 (43.8%) HBV, 40 (8.9%) HCV, 146 (32.4%) cirrhosis, 39 (8.7%) diabetes, 45 (10.0%) hypertension, 25 (5.6%) FED, 26 (5.8%) renal disease and 8 (1.8%) CEVD. Compared to chart review, the sensitivity, specificity, PPV and NPV of the algorithms for above conditions are respectively: (PLC) 100%, 99.44%, 97.94%, 100%; (HBV) 61.93%, 99.21%, 98.39%, 76.99%; (HCV) 72.50%, 99.51%, 93.55%, 97.37%; (cirrhosis) 78.77%, 98.36%, 95.83%, 90.61%; (diabetes) 97.44%, 98.05%, 82.61%, 99.75%; (hypertension) 95.00%, 99.72%, 97.44%, 99.45%; (FED) 71.43%, 100.00%, 100.00%, 99.09%; (renal disease) 96.15% 100.00% 100.00% 99.76%; (CEVD) 100.00%, 99.77%, 88.89%, 100.00%. ConclusionOur EMR case definitions of above conditions had high validity and could be applied to extract clinical variables including major liver diseases and comorbidities from Chinese EMR. This extracting method could be modified slightly for extracting other medical conditions from Chinese EMRs.
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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 | 0.104 | 0.234 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".