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README.md

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+---
+license: mit
+tags:
+  - tabular-classification
+  - healthcare
+  - synthetic-data
+  - ehr
+library_name: pytorch
+---
+
+# Patient Timeline Forecasting — ens_lr_gbdt_tx
+
+Predicts which of 40 target conditions are **newly** diagnosed in the five
+years after a patient's anchor date, from structured Synthea EHR events
+recorded strictly before it.
+
+Trained for the M31 research-intern take-home. **Synthetic data only. Not a
+clinical device, and not usable for any decision about a real person.**
+
+## Task
+
+- 3,514 Synthea patients, split 2,791 train / 365 validation / 358 test.
+- Anchor = last recorded encounter minus five calendar years, floored to midnight.
+- Label = **first-ever** diagnosis of the condition falls in `[anchor, anchor+5y]`.
+  A patient already diagnosed before the anchor is *prevalent*: structurally
+  negative, and reported separately rather than counted as an ordinary negative.
+
+## Results (validation, n=365)
+
+| metric | value |
+|---|---|
+| val_macro_auroc | 0.7700 |
+| val_macro_ap | 0.2681 |
+
+The validation set resolves differences of roughly **±0.01 macro AUROC** and no
+better. Per-condition AUROC on the rarest label (5 positives) carries a
+Hanley–McNeil 95% interval of about ±0.24. Numbers below that separation are
+reported but should not be read as rankings.
+
+## What the model is actually learning
+
+The anchor is defined from each patient's last encounter, so for the **42.9%**
+of training patients with a death date, the outcome window is exactly their
+last five years of life — 100% of those deaths fall within 30 days of the
+window end. A substantial part of the achievable signal is therefore
+"is this record about to end", which is a property of how the task was
+constructed rather than of clinical prediction. The same rule generated the
+test anchors, so this is not leakage, but it does bound how the results should
+be interpreted.
+
+## Leakage controls
+
+`DEATHDATE`, `HEALTHCARE_EXPENSES` and `HEALTHCARE_COVERAGE` are refused at
+load time. `STOP`-derived durations are excluded: the organisers blanked
+post-anchor stops in the test split, so such a feature would both leak and
+shift. Every fitted statistic — vocabulary, quantile edges, scalers — is fitted
+on training patients only. 63 automated checks cover this, including a grep
+test that no module outside the time utility parses a timestamp.
+
+## Limitations
+
+Synthea is not real EHR data. A discriminator separates it from MIMIC at
+AUC 0.999, and across 19 health datasets the winning classifier agreed between
+real- and synthetic-trained models in only 21–26% of cases. **Whichever model
+wins here, that ranking should not be assumed to transfer.**
+
+## Reproducing
+
+```bash
+git clone https://github.com/Sallamsaka/M31-Coding-Test && pip install -r requirements.txt
+./run_all.ps1
+```