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rutgers-cardio-research
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rutgers-DD-4source-new-labels-v2

PyTorch
cardiology
ecg
tissue-doppler-imaging
diastolic-dysfunction
binary-classification
attention
multimodal
Model card Files
xet
 Community
adhi29 commited on
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b4bf8b2
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Add CV outputs + iter 369 probability distribution plot + comparison vs v1; update README

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Adds the full 10-fold CV outputs (per-fold predictions, pooled OOF, metrics summary, ROC plots), a 2-panel probability-distribution plot (raw + Platt for iter 369 alone), and the 4-panel iter 264 vs iter 369 comparison plot. README updated to reference all four PNGs + cross_validation/.

Files changed (19) hide show
  1. .gitattributes +1 -0
  2. README.md +10 -2
  3. cross_validation/all_folds_predictions.csv +980 -0
  4. cross_validation/fold_01_predictions.csv +99 -0
  5. cross_validation/fold_02_predictions.csv +99 -0
  6. cross_validation/fold_03_predictions.csv +99 -0
  7. cross_validation/fold_04_predictions.csv +99 -0
  8. cross_validation/fold_05_predictions.csv +99 -0
  9. cross_validation/fold_06_predictions.csv +99 -0
  10. cross_validation/fold_07_predictions.csv +99 -0
  11. cross_validation/fold_08_predictions.csv +99 -0
  12. cross_validation/fold_09_predictions.csv +99 -0
  13. cross_validation/fold_10_predictions.csv +98 -0
  14. cross_validation/metrics_summary.txt +28 -0
  15. cross_validation/roc_auc.png +0 -0
  16. cross_validation/roc_cab_test.png +0 -0
  17. cross_validation/roc_curve.png +0 -0
  18. probability_distributions.png +0 -0
  19. probability_distributions_vs_v1.png +3 -0
.gitattributes CHANGED
@@ -35,3 +35,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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  probability_calibration.png filter=lfs diff=lfs merge=lfs -text
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  calibration_threshold_distributions.png filter=lfs diff=lfs merge=lfs -text
 
 
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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  probability_calibration.png filter=lfs diff=lfs merge=lfs -text
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  calibration_threshold_distributions.png filter=lfs diff=lfs merge=lfs -text
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+ probability_distributions_vs_v1.png filter=lfs diff=lfs merge=lfs -text
README.md CHANGED
@@ -57,7 +57,12 @@ Discrimination is threshold-free; the three operating points below illustrate al
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  > achievable operating point if the threshold were re-optimized on test; this
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  > number must not be used as a held-out estimate.
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- See [`probability_calibration.png`](probability_calibration.png) for the reliability diagram (raw vs Platt) and [`calibration_threshold_distributions.png`](calibration_threshold_distributions.png) for the full 5-calibrations × 5-threshold-rules sensitivity grid.
 
 
 
 
 
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  ---
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@@ -206,8 +211,11 @@ Required Python packages: `torch`, `numpy`, `pandas`, `scikit-learn`.
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  | `preprocessing.npz` | Fitted preprocessing stats: PCAIterativeImputer fill values, CrossSiteWaveformScaler, per-timepoint z-score, cross-modal residualization betas |
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  | `inference.py` | CLI inference script with two operating-point options |
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  | `calibration.json` | Platt fit (in-sample) + two operating-point scenarios |
 
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  | `probability_calibration.png` | Reliability diagram on CAB test (raw vs Platt) |
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- | `calibration_threshold_distributions.png` | 5-calibration × 5-threshold grid: shows test-set probability distributions and operating points |
 
 
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  | `README.md` | This file |
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  ---
 
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  > achievable operating point if the threshold were re-optimized on test; this
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  > number must not be used as a held-out estimate.
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+ See the visual artifacts shipped with this package:
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+
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+ - [`probability_distributions.png`](probability_distributions.png) — predicted-probability histograms on CAB test, split by ground-truth class, with the training-derived Youden threshold marked (raw + Platt-calibrated panels).
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+ - [`probability_calibration.png`](probability_calibration.png) — reliability diagram on CAB test (raw vs Platt) with bin counts.
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+ - [`probability_distributions_vs_v1.png`](probability_distributions_vs_v1.png) — 4-panel comparison of iter 264 (deployed v1) vs iter 369 (v2) across the three threshold scenarios above plus the test-set-optimal reference point.
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+ - [`calibration_threshold_distributions.png`](calibration_threshold_distributions.png) — extended 5-calibrations × 5-threshold-rules sensitivity grid (25 panels).
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  ---
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  | `preprocessing.npz` | Fitted preprocessing stats: PCAIterativeImputer fill values, CrossSiteWaveformScaler, per-timepoint z-score, cross-modal residualization betas |
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  | `inference.py` | CLI inference script with two operating-point options |
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  | `calibration.json` | Platt fit (in-sample) + two operating-point scenarios |
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+ | `probability_distributions.png` | iter 369 only: histograms split by class for raw + Platt-calibrated probabilities (CAB test), with training-derived thresholds marked |
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  | `probability_calibration.png` | Reliability diagram on CAB test (raw vs Platt) |
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+ | `probability_distributions_vs_v1.png` | 4-panel comparison of iter 264 (v1) vs iter 369 (v2) probability distributions and operating points |
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+ | `calibration_threshold_distributions.png` | Extended 5-calibration × 5-threshold grid (25 panels) — sensitivity analysis |
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+ | `cross_validation/` | 10-fold CV outputs: per-fold predictions, pooled OOF predictions, metrics summary, ROC plots |
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  | `README.md` | This file |
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  ---
cross_validation/all_folds_predictions.csv ADDED
@@ -0,0 +1,980 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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cross_validation/fold_01_predictions.csv ADDED
@@ -0,0 +1,99 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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cross_validation/fold_02_predictions.csv ADDED
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cross_validation/fold_05_predictions.csv ADDED
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cross_validation/metrics_summary.txt ADDED
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+ Cross-Validation Metrics Summary
2
+ Variant: 4source Label: DD_new
3
+ ============================================================
4
+
5
+ ── Per-Fold Metrics ─────────────────────────────────────────
6
+ Fold AUC APRC Acc F1 Sens Spec
7
+ 1 0.8011 0.3855 0.1020 0.1852 1.0000 0.0000
8
+ 2 0.7489 0.3023 0.1020 0.1852 1.0000 0.0000
9
+ 3 0.8250 0.3525 0.1020 0.1852 1.0000 0.0000
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+ 4 0.8352 0.4887 0.1020 0.1852 1.0000 0.0000
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+ 5 0.7920 0.4276 0.1020 0.1852 1.0000 0.0000
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+ 6 0.8591 0.5499 0.1020 0.1852 1.0000 0.0000
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+ 7 0.7670 0.3257 0.1122 0.2018 1.0000 0.0000
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+ 8 0.8934 0.6233 0.1122 0.2018 1.0000 0.0000
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+ 9 0.8443 0.4985 0.1122 0.2018 1.0000 0.0000
16
+ 10 0.7724 0.2941 0.1031 0.1869 1.0000 0.0000
17
+ ────────────────────────────────────────────────────────────
18
+ Mean 0.8138 0.4248 0.1052 0.1904 1.0000 0.0000
19
+ SD 0.0432 0.1064 0.0046 0.0075 0.0000 0.0000
20
+
21
+ ── Aggregate (all-fold pool) ─────────────────────────────────
22
+ AUC : 0.7482
23
+ APRC : 0.2441
24
+ Accuracy : 0.1052
25
+ F1-score : 0.1904
26
+ Sensitivity : 1.0000
27
+ Specificity : 0.0000
28
+ TP/TN/FP/FN : 103/0/876/0
cross_validation/roc_auc.png ADDED
cross_validation/roc_cab_test.png ADDED
cross_validation/roc_curve.png ADDED
probability_distributions.png ADDED
probability_distributions_vs_v1.png ADDED

Git LFS Details

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