Learning Health System for Breast Cancer: Pilot Project Experience
Bibliographic record
Abstract
PURPOSE: Clinicians need accurate and timely information on the impact of treatments on patient outcomes. The electronic health record (EHR) offers the potential for insight into real-world patient experiences and outcomes, but it is difficult to tap into. Our goal was to apply artificial intelligence technology to the EHR to characterize the clinical course of patients with stage III breast cancer. PATIENTS AND METHODS: Data from patients with stage III breast cancer who presented between 2013 and 2015 were extracted from the EHR, de-identified, and imported into the IBM Cloud. Specialized natural language processing (NLP) annotators were developed to extract medical concepts from unstructured clinical text and transform them to structured attributes. In the validation phase, these annotators were applied to 19 additional patients with stage III breast cancer from the same period. The resulting data were compared with that in the medical chart (gold standard) for nine key indicators. RESULTS: Information was extracted for 50 patients, including tumor stage (94% stage IIIA, 6% stage IIIB), age (28% 50 years or younger, 52% between 51 and 70 years, and 24% older than 70 years), receptor status (84% estrogen receptor positive, 74% progesterone receptor positive), and first treatment (72% surgery, 26% chemotherapy, 2% endocrine). Events in the patient's journey were compiled to create a timeline. For 171 data elements, NLP and the chart disagreed for 41 (24%; 95% CI, 17.8% to 31.1%). With additional manipulation using simple logic, the disagreement was reduced to six elements (3.5%; 95% CI, 1.3% to 7.5%; F1 statistic, 0.9694). CONCLUSION: It is possible to extract, read, and combine data from the EHR to view the patient journey. The agreement between NLP and the gold standard was high, which supports validity.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".