Comparative study of disease categories: EHR interrogation
Bibliographic record
Abstract
Objective: Although the advancement of electronic health records (EHRs) utilization in clinical research may allow for feasibility studies, and identify patients who are eligible for enrollment in clinical trials, it is a complex process to conduct clinical and translational research studies by merging data from different EMRs. Barriers and challenges such as data interoperability, lack of Health Insurance Portability and Accountability Act (HIPAA) compliant platforms for data integration, and the lack of real efforts to resolve these issues make it harder to conduct these studies. However, it is imperative to note that leveraging EHRs to counterbalance these challenges is an area of intense interest and data sharing from hospitals may enable clinical research with large samples for a moderate or large effect size. To inform this issue, we worked across urban hospitals with data extracted from different systems, for patients diagnosed with diabetes and myocardial infarction, in the year 2013.Methods: Using ICD 9 codes for diabetes (25,000) and myocardial infarction (41,000), data were extracted from urban hospitals. The data were then cleaned, merged using common fields, and analyzed. It is important to note that ICD 9 was used instead of ICD 10 because one of the hospitals had an already existing dataset extracted from EHR using ICD 9. In addition, the hospital with the ICD 9 dataset provided the original data with the needed variables prior to the grant application that made this project possible.Results: The result showed that patients discharged in 2013 from the selected urban hospitals with MI, were 3.8 times more likely to die while in admission and 4.2 times in MI+DM patients. However, race and gender were not significant in the adjusted model. Variables that impacted this critical result were age of the patients, followed by low density lipoprotein, systolic blood pressure and body mass index.Conclusions: Comparative studies for preliminary studies through EHR interrogation is the future. This project has confirmed that similar studies should be encouraged and may lead to preventive health education that may ultimately prevent higher mortality rate in certain population. This project is a proof of concept of how data from different EHR platforms can be used to conduct a comparative study by a direct hospitals EHR interrogation, without additional time needed in bedside data collection or purchase of already collected datasets.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".