iatro-clintrace 0.0.1.dev2026070802

ClinTrace clinical-course compression, grounding, and longitudinal decision modeling

Iatro ClinTrace

ClinTrace is a longitudinal clinical-course model for HCC notes. Its job is to turn free-text clinical notes into a reusable course representation that can support treatment-pathway reasoning, evidence-grounded decision completion, and downstream model integration.

Research use only. ClinTrace is not a medical device and must not be used for clinical diagnosis, treatment selection, triage, or patient management.

The authoritative design record is docs/clintrace_total_design.md. This README is the open-source entrypoint: it describes the package boundary, public command surface, and controlled-asset contract.

Table of Contents

• Scientific Contract
• Components
• Release Boundary
• Installation
• Training Workflow
  • 1. Build Source IAC Assets
  • 2. Build or Import Compressed Text Assets
  • 3. Build and Check Paired IAC Assets
  • 4. Train, Select, and Export the Compressor
  • 5. Build Frozen Note Features
  • 6. Build Decision Supervision
  • 7. Build Direct-Token and Decoder Planes
  • 8. Build History-State and Decision-Contrast Assets
  • 9. Train the ClinTrace Decision Expert
  • 10. Evaluate and Freeze Release Candidates
• Configuration
• Package Layout
• Open-Source Hygiene
• Optional LLM Client Utilities

Scientific Contract

ClinTrace learns from clinical notes, not from manually supplied decision-chain labels at inference time.

input
  Stage-A/tokenized note representations
  observable note title/type text
  natural longitudinal order

supervision and audit
  LLM-extracted E/P/X/O chains
  timeline-derived labels
  history-state summaries

output
  course expert records
  grounded evidence spans
  ranked open-world decision candidates

E/P/X/O chains are supervision and evaluation assets. They are not deployment inputs. Timeline-derived fields such as document role, label type, pathway labels, and decision-bearing flags are also not model inputs in the formal expert, because they are not guaranteed to exist when a user sends a new note.

The central modeling target is:

history state + observed clinical evidence -> plausible P/X/O decision candidates

This is intentionally open-world. Missing labels in the record are treated as unobserved, not as clinical negatives. The model is evaluated primarily by whether documented decisions are ranked highly and whether generated evidence and decision spans are clinically inspectable.

Components

iatro.clintrace.compressor
  Single-note compression into fixed latent note tokens.

iatro.clintrace.routing
  Compactness router used before tokenization to choose compression vs direct
  token representation.

iatro.clintrace.grounding
  Full-note EXPO grounding: evidence, plan, executed action, and outcome span
  supervision from extracted decision chains.

iatro.clintrace.completion
  Observed-evidence decision completion within a note.

iatro.clintrace.longitudinal
  History-state-conditioned decision completion and decision-contrast diagnostics.

iatro.clintrace.timeline
  Patient-level chronological note indexing.

iatro.clintrace.inspect
  Local IAC asset browsers for development and audit.

Release Boundary

This GitHub repository contains source code, configuration, prompt contracts, tests, and documentation only. Clinical data, extracted chains, generated caches, model weights, evaluation predictions, API credentials, and training outputs are not part of the GitHub repository.

Model and auxiliary binary assets are produced by the controlled full training/validation pipeline for a separate gated Hugging Face release. They must not be committed to GitHub, including through Git LFS.

Tracked GitHub content:

src/                             Python package
configs/                         reproducible training/evaluation configs
prompts/                         versioned LLM prompt contracts
docs/                            design and method notes
tests/                           unit tests
README.md, LICENSE, pyproject.toml

Expected local asset classes:

data/00_source/                  raw clinical_text IAC packs
data/01_compressed/              compressed note tables keyed by doc_id
data/02_paired/                  source/compressed paired IAC packs
data/03_timeline_index/          longitudinal note indexes
data/04_clintrace_features/      frozen compressor feature caches
data/05_decision_chains/         extracted decision-chain JSONL
data/06_decision_supervision/    compiled EXPO supervision assets
data/07_grounding_features/
                                 tokenizer/direct-note feature caches
data/08_patient_state_history/   history-state text summaries
runs/                            ignored training/evaluation run outputs
artifacts/compressor/            selected local compressor artifacts
artifacts/clintrace/             selected local ClinTrace planes and auxiliaries
artifacts/hf/clintrace/          gated Hugging Face release tree
dist/                            ignored release/build output directory

Legacy local caches may still use older non-contiguous directory numbers. The public workflow uses the contiguous layout above.

data/, artifacts/, runs/, result/ legacy outputs, and dist/ are ignored by git. Public examples should use de-identified toy data or externally releasable fixtures only.

Gated Hugging Face artifacts are generated only as part of the complete controlled training and validation chain. Do not create or commit ad hoc local asset bundles from intermediate results.

Installation

Use the shared project environment.

pip install -e .
pip install -e ".[train]"
pip install -e ".[serve]"
pip install -e ".[dev]"

Dependency groups:

.[train]  training, cache generation, and compression-data preparation
.[serve]  optional vLLM-compatible local serving
.[dev]    tests and packaging checks

Training Workflow

The public package exposes the same component order used by the full training run. All commands below read and write ignored local assets under data/, runs/, and artifacts/; those outputs are never committed to GitHub.

1. Build Source IAC Assets

Start from a de-identified document table in JSONL, CSV, or TSV. Required columns are:

patient_id      stable de-identified patient identifier
doc_id          stable de-identified document identifier, unique within dataset
text            clinical note text

Optional columns are:

doc_type        observable note title/type
doc_timestamp   sortable clinical timestamp string
episode_index   integer visit/admission index, -1 if unknown
encounter       optional encounter identifier
doc_category    optional source category, defaults to 病案文书
source          optional source-system label

Pack the table into a clinical_text IAC:

clintrace build source-iac \
  --input data/source_documents/site_a.jsonl \
  --output data/00_source/site_a.iac \
  --institution site_a \
  --overwrite

The source pack builder sorts documents within each patient by episode_index, doc_timestamp, and doc_id. It skips rows with missing required fields, duplicate doc_id, or empty text. It reports:

input_rows=<input table rows>
patients=<packed patient count>
docs=<packed document count>
duplicate_doc_ids=<skipped duplicate document ids>
empty_text=<skipped empty documents>
missing_required=<missing required-field counts>
text_raw_mb=<UTF-8 text payload size>
output=<source IAC path> size_mb=<written pack size>

Verify the source pack:

clintrace build source-iac \
  --input data/source_documents/site_a.jsonl \
  --output data/00_source/site_a.iac \
  --institution site_a \
  --verify

Verification must report:

verify_docs == verify_expected_docs
verify_unique_doc_ids == verify_expected_docs
verify_mismatches == 0

The resulting asset is:

data/00_source/{site}.iac
  clinical_text pack containing original de-identified notes.

2. Build or Import Compressed Text Assets

Create or import compressed note text before compressor training. This asset is a normal JSONL, CSV, or TSV table, not an IAC pack. Required columns are:

doc_id             same de-identified document identifier as source IAC
compressed_text    compressed clinical note text

Accepted aliases for compressed_text are text and output. Optional columns are doc_type, doc_timestamp, and episode_index; when omitted, the source IAC metadata is used.

data/01_compressed/{site}.jsonl
  compressed-note table keyed by doc_id.

The compression method is outside this public contract. It may be produced by a human-reviewed pipeline, a local model, or an external system; ClinTrace only requires aligned doc_id values and clinically readable compressed text. If a dataset requires de-identification or identifier alignment, complete that data preparation before building ClinTrace pairs. The open-source package does not define or distribute private identifier-mapping protocols. For the optional LLM client wrapper that uses prompts/compressor_zh.md and writes a paired-iac-ready JSONL, see Optional LLM Client Utilities.

3. Build and Check Paired IAC Assets

Once the source IAC and compressed table share de-identified document identifiers, build the source/compressed training pairs:

data/02_paired/{site}.iac
  source/compressed clinical_text_pair pack used by the compressor and router.

Build a paired source/compressed pack:

clintrace build paired-iac \
  --source-iac data/00_source/site_a.iac \
  --compressed-input data/01_compressed/site_a.jsonl \
  --output data/02_paired/site_a.iac \
  --institution site_a \
  --overwrite

paired-iac is the only command that writes compressed text into an IAC asset. There is no standalone compressed IAC in the public workflow. If a dataset needs de-identification or identifier mapping to make source and compressed IDs align, complete that preparation before paired-iac. The paired IAC itself is source text paired with compressed text; it is not a de-identified/non-de-identified pair.

Record the following IAC-level acceptance metrics before model training:

patients=<paired patient count>
paired_docs=<paired document count>
source_docs=<source document count>
compressed_rows=<compressed input table rows>
compressed_docs=<unique usable compressed document count>
duplicate_doc_ids=<skipped duplicate compressed document ids>
missing_compressed=<source docs without compressed counterpart>
empty_source=<skipped empty source docs>
empty_compressed=<skipped empty compressed docs>
missing_required=<missing required-field counts>
source_raw_mb=<UTF-8 source text payload size>
compressed_raw_mb=<UTF-8 compressed text payload size>
output=<paired IAC path> size_mb=<written pack size>

Verify the paired pack:

clintrace build paired-iac \
  --source-iac data/00_source/site_a.iac \
  --compressed-input data/01_compressed/site_a.jsonl \
  --output data/02_paired/site_a.iac \
  --institution site_a \
  --verify

Verification must report:

verify_docs == verify_expected_docs
verify_unique_doc_ids == verify_expected_docs
verify_mismatches == 0

Then inspect both the raw pack and paired pack before starting a long run:

clintrace inspect iac data/00_source/site_a.iac --head 5
clintrace inspect pairs -i site_a --head 5

The IAC acceptance gate is: all expected documents are paired exactly once, source/compressed text round-trips without mismatch, document timestamps and episode indexes are populated enough for timeline construction, and sampled records are clinically readable after de-identification.

4. Train, Select, and Export the Compressor

The compressor maps a clinical note to fixed note tokens. Train it from the paired source/compressed IAC:

clintrace train compressor --config configs/compressor/train.yaml

Training writes checkpoints and metrics under runs/ or the configured compressor output directory. Select the checkpoint by the predeclared validation rule, then export the release-sized compressor artifact into the ignored artifacts/compressor/ tree:

clintrace build compressor-artifact \
  --checkpoint runs/compressor/checkpoints/course_encoder_step5500.pt \
  --output artifacts/compressor/clintrace_compressor_qwen35_2b_n32_d768_step5500.pt

Run a single-note sanity check on the exported artifact:

clintrace compress \
  --checkpoint artifacts/compressor/clintrace_compressor_qwen35_2b_n32_d768_step5500.pt \
  --input tmp/source_note.txt \
  --output tmp/compressed_note.txt

If a validated compressor artifact already exists, start from this sanity check and continue with feature generation.

5. Build Frozen Note Features

Create longitudinal indexes and frozen Stage-A note-token caches. These caches are the model input substrate for downstream decision training.

clintrace build timeline
clintrace build compressor-features --checkpoint artifacts/compressor/clintrace_compressor_qwen35_2b_n32_d768_step5500.pt

6. Build Decision Supervision

Compile the extracted EXPO chains into supervision assets. The chain labels and spans are used only as training/evaluation targets, not as inference inputs.

clintrace build grounding-supervision --overwrite

7. Build Direct-Token and Decoder Planes

Build the direct-token feature plane used for compact or already-compressed text, then cache per-note grounding features.

clintrace build decoder-plane
clintrace build grounding-features --overwrite

8. Build History-State and Decision-Contrast Assets

History-state summaries provide longitudinal patient context for admission-level training. Decision-contrast pairs are conservative EXPO bundle comparisons used to reduce clinically implausible near-neighbor confusion.

clintrace llmc history --help
clintrace build decision-contrast

9. Train the ClinTrace Decision Expert

The decision expert is trained in three ordered components. The component names describe the capability being learned, not separate deployable experts.

clintrace train router
clintrace train grounding --overwrite
clintrace train completion --initialization grounding_init --overwrite
clintrace train longitudinal --initialization completion_init --eval-splits validation test external --overwrite

grounding learns full-note EXPO span grounding. completion learns observed-evidence to P/X/O decision completion. longitudinal adds H1 history state conditioning while keeping EXPO grounding and completion active.

10. Evaluate and Freeze Release Candidates

Evaluate every selected checkpoint on validation, internal test, and external splits. The gated release candidate is chosen from the complete validation chain, not from an ad hoc artifact bundle.

clintrace evaluate grounding --split validation
clintrace evaluate completion --checkpoint runs/clintrace_completion_grounding_init/clintrace_decision_expert.pt --split test
clintrace evaluate longitudinal --checkpoint runs/clintrace_longitudinal_completion_init/clintrace_longitudinal.pt --split test
clintrace evaluate decision-contrast --help

After the complete validation chain is frozen, release assets are staged under artifacts/hf/clintrace/. That directory is the local Hugging Face gated-release root and remains ignored by git.

Configuration

Configs are grouped by component:

configs/compressor/              compressor training and local smoke configs
configs/decision/default.yaml     grounding/completion/longitudinal defaults

Package Layout

src/iatro/clintrace/
  compressor/
  routing/
  grounding/
  completion/
  longitudinal/
  timeline/
  inspect/

Top-level scripts/ is intentionally empty for the public package. Stable functionality belongs in the package and is exposed through clintrace.

Open-Source Hygiene

Do not commit:

clinical data
LLM extraction outputs containing protected text
model checkpoints or generated feature caches
runs folders
local credentials or API keys
Hugging Face gated release assets
throwaway intermediate files

Institution-specific names are avoided in public code and documentation. Local asset aliases should use neutral identifiers such as site_a and site_b.

Optional LLM Client Utilities

clintrace llmc commands are wrappers around OpenAI-compatible chat endpoints. They are client-side data-preparation utilities, not ClinTrace build steps.

clintrace llmc compress    prompts/compressor_zh.md -> compressed-note JSONL
clintrace llmc expo        prompts/expo_chain_extraction_zh.md -> EXPO chain JSONL
clintrace llmc history     prompts/history_state_h1_zh.md -> H1 history-state JSONL

llmc compress reads a source IAC, applies the versioned compression prompt, and appends compressed-note rows to a JSONL file that can be consumed directly by clintrace build paired-iac --compressed-input.

The output JSONL contains at least:

doc_id
patient_id
institution
doc_type
doc_timestamp
episode_index
compressed_text
usage

Default execution targets a local OpenAI-compatible server so clinical text stays on the host.

export OPENAI_API_BASE=http://127.0.0.1:8000/v1
export OPENAI_API_KEY=local
export OPENAI_MODEL=qwen36

clintrace llmc compress \
  --institution site_a \
  --source-iac data/00_source/site_a.iac \
  --prompt prompts/compressor_zh.md \
  --out data/01_compressed/site_a.jsonl \
  --model qwen36 \
  --workers 16 \
  --thinking disabled

Useful preparation checks:

clintrace llmc compress \
  --institution site_a \
  --source-iac data/00_source/site_a.iac \
  --out data/01_compressed/site_a.jsonl \
  --dry-run

clintrace build paired-iac \
  --source-iac data/00_source/site_a.iac \
  --compressed-input data/01_compressed/site_a.jsonl \
  --output data/02_paired/site_a.iac \
  --institution site_a \
  --overwrite

Rows are resumable by doc_id: rerunning the wrapper skips documents that already have a non-empty compressed_text in the output file. Non-local API endpoints require --allow-external-endpoint; only use that with an approved clinical-data processing endpoint.

EXPO and history-state preparation use the same endpoint policy:

clintrace llmc expo \
  --input data/01_compressed/site_a.jsonl \
  --out data/05_decision_chains/results.jsonl \
  --label-vocab data/06_decision_supervision/label_vocab.json \
  --model qwen36 \
  --workers 16

clintrace llmc history \
  --institution site_a \
  --compressed-input data/01_compressed/site_a.jsonl \
  --timeline-dir data/03_timeline_index \
  --output data/08_patient_state_history/site_a.jsonl \
  --model qwen36 \
  --workers 16