Value-Based Healthcare Translational Data Analytics using the Problem Oriented Medical Record Graph Representation
Bibliographic record
Abstract
Translational data analytics play an important role in developing, applying and evaluating health outcomes to improve the effectiveness, safety, and patient experiences of care. Through this integrative approach of linking the bedside clinical practice with the benchside biomedical research relevant information of different varieties from multiple sources can provide a more accurate patterns contributing to health outcomes. In this article we are introducing an exploratory analytics approach to generate knowledge graphs from patient cases described via the SOAP note at the bedside and expand this graph through building attributes like medications to what the bench can provide on research related to these attributes. This exploratory approach has been applied to the problem of identifying polypharmacy knowledge graphs. However, we are proposing to used the generated knowledge graphs using an autoencoder to decode neighborhoods of polypharmacy and label nodes like drugs as candidate for deprescribion or not. The autoencoder is our next phase of research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".