Physician Experience Design (PXD): More Usable Machine Learning Prediction for Clinical Decision Making.
Bibliographic record
Abstract
Delirium is an acute neurocognitive disorder, which is difficult to identify and predict. Using GEMINI, Canada's largest hospital data and analytics study, we had a labeled sample of around 4,000 cases with approximately 25% of cases being labeled as having delirium. Based on this labeled data, we developed machine learning (ML) models and interacted with physicians to interpret the ML models and their predictions. We developed a preliminary Explainable Artificial Intelligence (XAI) framework for physician experience design (PXD) to improve the uptake of ML models by improving the transparency of model results, thereby increasing physician trust in models as well as the uptake of model results for clinical decision making. We developed our PXD approach first with Conceptual Investigation to collect and extract physicians' feedback on ML models and their evaluation requirements. We carried out a case study, working closely with the physicians in a participatory design process to develop a dashboard that presents ML delirium identification results interactively based on physician selections and inputs. In this approach a physician-preferred ML model for clinical decision making is selected through PXD evaluation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".