Extracting Information for Generating A Diabetes Report Card from Free Text in Physicians Notes
Bibliographic record
Abstract
Achieving guideline-based targets in patients with diabetes is crucial for improving clinical outcomes and preventing long-term complications. Using electronic heath records (EHRs) to identify high-risk patients for further intervention by screening large populations is limited because many EHRs store clinical information as dictated and transcribed free text notes that are not amenable to statistical analysis. This paper presents the process of extracting elements needed for generating a diabetes report card from free text notes written in English. Numerical measurements, representing lab values and physical examinations results are extracted from free text documents and then stored in a structured database. Extracting diagnosis information and medication lists are work in progress. The complete dataset for this project is comprised of 81,932 documents from 30,459 patients collected over a period of 5 years. The patient population is considered high risk for diabetes as they have existing cardiovascular complications. Experimental results validate our method, demonstrating high precision (88.8--100%).
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".