| --- |
| license: other |
| tags: |
| - missing-data |
| - imputation |
| - benchmark |
| - healthcare |
| --- |
| |
| # Missing-Data Prediction Benchmark — results |
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| Two experiments on medical datasets under simulated missingness (MCAR/MAR/MNAR, |
| rates 0.1–0.5, seeds 42/1/7), reported as **mean [95% CI]**. |
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| This release contains the complete Information-Acquisition benchmark at a uniform missing |
| rate of 0.3. Scenario 2 includes local AFA-engine results, curves, seed-aggregated CI |
| tables, qualitative examples, classification AUROC, and per-engine cached/uncached token |
| accounting across five classification datasets (`arrhythmia`, `breast_cancer`, `gallstone`, |
| `heart_disease`, `parkinsons`) and four regression datasets (`diabetes`, `nhanes`, |
| `who_life_expectancy`, `medical_cost`). |
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| Scenario 1 uses `gpt-5.6-terra` to select features and predict directly. It is complete for |
| the same classification and regression dataset sets. Classification Scenario 1 reports |
| accuracy, F1, precision, and recall are preserved from the original label-only query. |
| Classification AUROC comes from a separate probability replay with the original selection path |
| unchanged; this mixed provenance is noted in the A/B report. Regression Scenario 1 predicts a |
| continuous numeric target and reports RMSE, MAE, and R2. MIMIC Scenario 1 is absent because |
| credentialed MIMIC-derived records are not sent to third-party APIs. |
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| No patient-level records, raw MIMIC data, trained models, or LLM traces containing MIMIC |
| features are published. |
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| ### Classification Scenario 1 vs Scenario 2 |
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| | dataset | mech | LLM acc | AFA acc | LLM F1 | AFA F1 | LLM AUROC | AFA AUROC | best AFA | LLM tokens | S2 cached | S2 uncached | ↓ cached | ↓ uncached | |
| |---|---|--:|--:|--:|--:|--:|--:|---|--:|--:|--:|--:|--:| |
| | arrhythmia | MAR | 0.458 | 0.736 | 0.44 | 0.734 | 0.494 | 0.772 | random_forest | 10,516,156 | 31,992 | 417,730 | **99.7%** | 96.0% | |
| | arrhythmia | MCAR | 0.473 | 0.74 | 0.456 | 0.738 | 0.431 | 0.784 | random_forest | 10,544,703 | 31,992 | 404,444 | **99.7%** | 96.2% | |
| | arrhythmia | MNAR | 0.513 | 0.707 | 0.495 | 0.704 | 0.423 | 0.771 | random_forest | 10,926,848 | 31,992 | 414,435 | **99.7%** | 96.2% | |
| | breast_cancer | MAR | 0.921 | 0.977 | 0.921 | 0.977 | 0.979 | 0.996 | smart | 348,937 | 4,835 | 56,524 | **98.6%** | 83.8% | |
| | breast_cancer | MCAR | 0.927 | 0.98 | 0.927 | 0.979 | 0.979 | 0.996 | mim_smim | 373,315 | 4,191 | 13,165 | **98.9%** | 96.5% | |
| | breast_cancer | MNAR | 0.927 | 0.974 | 0.927 | 0.974 | 0.983 | 0.995 | smart | 348,073 | 4,835 | 67,905 | **98.6%** | 80.5% | |
| | gallstone | MAR | 0.495 | 0.771 | 0.483 | 0.769 | 0.543 | 0.838 | hist_gb | 823,103 | 5,990 | 50,355 | **99.3%** | 93.9% | |
| | gallstone | MCAR | 0.495 | 0.755 | 0.482 | 0.755 | 0.484 | 0.814 | mim_smim | 830,642 | 6,043 | 43,630 | **99.3%** | 94.7% | |
| | gallstone | MNAR | 0.51 | 0.771 | 0.499 | 0.77 | 0.49 | 0.817 | random_forest | 814,237 | 6,043 | 48,555 | **99.3%** | 94.0% | |
| | heart_disease | MAR | 0.7 | 0.844 | 0.698 | 0.844 | 0.78 | 0.903 | smart | 197,635 | 2,068 | 18,610 | **99.0%** | 90.6% | |
| | heart_disease | MCAR | 0.694 | 0.856 | 0.693 | 0.855 | 0.672 | 0.893 | mim_smim | 208,505 | 2,068 | 11,775 | **99.0%** | 94.4% | |
| | heart_disease | MNAR | 0.694 | 0.833 | 0.693 | 0.833 | 0.768 | 0.899 | smart | 198,665 | 2,068 | 18,187 | **99.0%** | 90.8% | |
| | parkinsons | MAR | 0.761 | 0.897 | 0.776 | 0.889 | 0.841 | 0.961 | hist_gb | 159,996 | 3,368 | 19,475 | **97.9%** | 87.8% | |
| | parkinsons | MCAR | 0.752 | 0.889 | 0.767 | 0.887 | 0.87 | 0.931 | hist_gb | 155,967 | 3,322 | 15,660 | **97.9%** | 90.0% | |
| | parkinsons | MNAR | 0.744 | 0.872 | 0.759 | 0.873 | 0.836 | 0.948 | xgboost | 164,584 | 3,373 | 14,908 | **98.0%** | 90.9% | |
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| ## How to read (no download needed) |
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| Every CSV opens in HuggingFace's Dataset Viewer (sortable, in-browser): |
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| - `experiment1_prediction/prediction_classification_long.csv` / `prediction_regression_long.csv` — **one tidy row per (method, dataset, mechanism, rate, seed)** with all metrics + status. This is the clean, flat view of every run (no folder-diving). |
| - `experiment1_prediction/classification_mean_ci.csv` / `regression_mean_ci.csv` — the same, collapsed to mean [95% CI] over seeds (blank cells carry a `Status`). |
| - `experiment2_acquisition/acquisition_ab_report.csv` — matched-sample classification Scenario 1 vs Scenario 2 accuracy and token comparison. |
| - `experiment2_acquisition/curves_long.csv` — per-budget Scenario 2 trajectories and Scenario 1 trajectories for both tasks. |
| - `experiment2_acquisition/acquisition_curves_ci.csv` / `acquisition_final_ci.csv` — seed-aggregated metrics for both scenarios. |
| - `experiment2_acquisition/scenario2_method_token_accounting.csv` — per-engine cached and uncached Scenario-2 token accounting. |
| - `DATA_DICTIONARY.md` — column definitions and status meanings. |
| - `raw_prediction/` — per-cell raw Exp-1 metric JSON (in-scope datasets, canonical layout, see raw_prediction/README_LATEST.md). |
| - `raw_acquisition/` — per-cell raw complete Scenario-1 LLM and Scenario-2 AFA outputs (no model weights). |
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| ## Data-use / MIMIC-III notice |
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| One regression dataset (`mimic_lengthofstay`) is derived from **MIMIC-III**, credentialed |
| under the PhysioNet Data Use Agreement. This repository contains only **aggregate derived |
| metrics** — no patient-level records, no raw MIMIC data, and no models trained on MIMIC. |
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| Generated 2026-09-09 by `scripts/reporting/prepare_hf_upload.py`. |
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