fms-ehrs runs the model steps used by ../input-representation-benchmark. It turns event tables into token sequences, trains models, extracts hidden model feature vectors, and runs prediction tasks. The benchmark repository controls experiment scheduling and final statistics assembly.

• fms_ehrs/scripts/tokenize_w_config.py
• fms_ehrs/scripts/tune_model.py
• fms_ehrs/scripts/train_representation.py
• fms_ehrs/scripts/extract_hidden_states.py
• fms_ehrs/scripts/transfer_rep_based_preds.py
• fms_ehrs/scripts/aggregate_version_preds.py
• fms_ehrs/scripts/eval_token_ce.py

Older scripts were moved to deprecated/.

The current benchmark trains 28 model settings under the same one-epoch training limit:

• Experiment 1 tests numeric bin size, reference-range anchoring, and whether code and value are merged into one token.
• Experiment 2 tests value methods (discrete, soft, xval, xval_affine) and time methods (none, age, rope).
• Experiment 3 tests vocabulary mapping arms (native, clif_mapped, rand_mapped, freq_mapped) with the discrete + rope setting.

The full benchmark defines 30 outcomes. Each experiment evaluates 29 outcomes because the ICU outcome differs between Experiments 1-2 and Experiment 3.

• tokenize MEDS event tables from YAML configuration files
• train sequence models
• rebuild value support modules during extraction when needed
• extract final model feature vectors from first-24-hour token timelines
• fit prediction models and save prediction payloads
• aggregate prediction payloads into metrics, confidence intervals, and paired comparison tables

• fms_ehrs/config/mimic-meds.yaml
• fms_ehrs/config/mimic-meds-ed.yaml
• fms_ehrs/config/mimic-meds-exp3-icu.yaml

Older CLIF configs live under deprecated/config/.

For current Experiment 3 runs, mimic-meds-exp3-icu.yaml tokenizes LAB and VITAL event blocks.

• First-24-hour tokenized timelines are the extraction surface for prediction features.
• xval and xval_affine runs depend on both numeric_stats.json and representation_mechanics.pt.
• aggregate_version_preds.py writes per-family metrics and paired tables. The benchmark repository then builds combined reporting tables.

• This repository covers the model-side path: tokenization, training, extraction, and prediction output generation.
• For the paper's reported statistics files, figure inputs, and metric audit surfaces, see the Statistics files for Reproducibility section in ../input-representation-benchmark/README.md.

slurm/ is now a pointer directory. Archived launchers are in deprecated/slurm/.

uv venv --python="$(which python3)" venv
. venv/bin/activate
uv pip install --torch-backend=cu128 --link-mode=copy -e .

• fms_ehrs/scripts/README.md: active script inventory
• fms_ehrs/tests/README.md: unit and dry-run audit layout
• docs/layout.md: repo layout
• docs/surface_inventory.md: active/utility/deprecated classification
• notes/README.md: maintained notes
• deprecated/README.md: archived material
• ../input-representation-benchmark/README.md: benchmark-level run path