VERONICA: Visual Analytics for Identifying Feature Groups in Disease Classification
Bibliographic record
Abstract
The use of data analysis techniques in electronic health records (EHRs) offers great promise in improving predictive risk modeling. Although useful, these analysis techniques often suffer from a lack of interpretability and transparency, especially when the data is high-dimensional. The emergence of a type of computational system known as visual analytics has the potential to address these issues by integrating data analysis techniques with interactive visualizations. This paper introduces a visual analytics system called VERONICA that utilizes the natural classification of features in EHRs to identify the group of features with the strongest predictive power. VERONICA incorporates a representative set of supervised machine learning techniques—namely, classification and regression tree, C5.0, random forest, support vector machines, and naive Bayes to support users in developing predictive models using EHRs. It then makes the analytics results accessible through an interactive visual interface. By integrating different sampling strategies, analytics algorithms, visualization techniques, and human-data interaction, VERONICA assists users in comparing prediction models in a systematic way. To demonstrate the usefulness and utility of our proposed system, we use the clinical dataset stored at ICES to identify the best representative feature groups in detecting patients who are at high risk of developing acute kidney injury.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".