Explainable Machine Learning Models for Pneumonia Mortality Risk Prediction Using MIMIC-III Data
Bibliographic record
Abstract
To gain trust, machine learning (ML) models used in high stake applications such as clinical decision support need to provide explainable behaviours and outputs. To assess whether interpretable explanations can be obtained without sacrificing prediction performance, we compare using “black box” versus “glass box” models for predicting the mortality risk of patients diagnosed with pneumonia, using data in the MIMIC-III dataset. We examine five types of black box models: random forest (RF), support vector machine (SVM), gradient boosting classifier (GBC), AdaBoost (ADA), and multilayer perceptron (MLP), and three types of glassbox models: K-nearest neighbor (KNN), explainable boosting machine (EBM), and generalized additive models (GAM). When trained using 417 features, a black box RF model performs best with AUC of 0.896. With the feature set size reduced to 19, an EBM model performs the best with AUC 0.872. Both models exceed the AUC of 0.661, the best previously reported for the task. Our results suggest that ML models with inbuilt explainability may provide prediction power as attractive as black box models.
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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 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".