Scope note: This repository was renamed from oac-clinical-transport-health-v1 to remove misleading naming. It is a synthetic-data operations/observability logistic-regression model for infrastructure system-health monitoring. It is not a clinical, diagnostic, or medical-decision model, has not been validated for any health-domain use, and must not receive PHI or clinical content.

OAC System Health v1

OAC System Health v1 is a tiny, dependency-free logistic-regression model for synthetic operational transport telemetry. It emits a non-authoritative operator-attention advisory. The repository is staged from the canonical source at szl-holdings/szl-forge.

Companion data: synthetic transport observability. Integration source: transport gateway.

Critical boundary

This is not a medical, diagnostic, prognostic, triage, treatment, or clinical decision model. It was not trained or validated on patients, laboratory results, specimens, orders, assays, physical devices, or a health-care site. It must never receive PHI or clinical/result content.

The model cannot:

• accept, reject, or acknowledge an HL7 message;
• command or identify a medical device;
• interpret, validate, autoverify, route, or release a result;
• authorize clinical use; or
• establish regulatory, privacy, security, or site acceptance.

Inputs

The kernel requires exactly eight operational fields:

Unknown, missing, non-finite, out-of-range, identity-like, HL7, FHIR, patient, specimen, order, and result fields fail closed.

Run without third-party packages

python -I -B oac_operational_health.py \
  --model model.json \
  --receipt artifact_receipt.json \
  --input example_input.json

The kernel verifies the model SHA-256 from artifact_receipt.json before inference. Output includes operator_attention_score, the validation-selected threshold, the boolean advisory, per-feature contributions, and an explicit all-false authority map.

The receipt is a reproducibility/mismatch control, not a signature or external trust root. A trusted deployment must pin the Hub commit and verify it through its own software-supply-chain policy; replacing both the model and receipt can otherwise bypass this local comparison.

Training and evaluation

The model is batch-gradient-descent logistic regression implemented using only the Python standard library. A fixed seed generates 768 training, 192 validation, and 240 test examples. The receipt records exact split metrics and hashes. Validation chooses the decision threshold; the test split is otherwise held out.

The 240-row synthetic test split reports balanced accuracy 0.7414, precision 0.4124, recall 0.7843, F1 0.5405, and ROC AUC 0.8302 at threshold 0.16. These are baseline implementation results, including 57 false positives and 11 false negatives; they do not justify production promotion.

All reported metrics are from generated synthetic examples. The score is not production-calibrated and the metrics must not be generalized to a real transport, analyzer, laboratory, patient population, or clinical workflow.

License

Apache-2.0. See LICENSE.