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Research Pipeline Platform

A reusable biomedical AI research platform providing infrastructure for research memory, experiment tracking, dataset governance, bias management, audit workflows, literature management, hypothesis tracking, evidence graphs, artifact provenance, benchmarking, model governance, deployment readiness, and reproducibility.

This platform supports multiple biomedical modalities: ECG, EEG, medical imaging, clinical NLP, EHR, and signal processing.

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Subsystem Purpose
memory/ Project state, decisions, assumptions, risks, operational memory
governance/ Bias register, leakage register, reproducibility rules, ethics
research/ Hypotheses, evidence graph, artifact lineage (PIPE-001A)
literature/ Paper summaries, literature tracking
audits/ Intake, dataset, architecture, training, evaluation, deployment, postmortem audits
datasets/ Registry, intake audits, metadata, policies, versioning
models/ Model registry, model cards, checkpoints, evaluations
experiments/ Experiment lifecycle management
benchmarks/ Benchmark definitions, leaderboards, protocols
deployment/ Deployment configurations (local, remote, cloud)
monitoring/ Training, infrastructure, cost, scientific drift monitoring
clinical/ Clinical ontologies, regulations, phenotypes
multimodal/ Modality scaffolding (ECG, EEG, EHR, imaging, NLP, wearables)
reports/ Milestone, audit, deployment reports
templates/ Reusable document templates
resources/ Reference library for models, datasets, standards
docs/ Platform documentation
tooling/ Automation scripts, validators, generators

First Implementation

The ECG foundation-model project lives at implementations/ecg_foundation/.

Principles

  1. All conclusions require audit evidence.
  2. Every experiment originates from a hypothesis.
  3. Every claim cites evidence (supporting and conflicting).
  4. All datasets require intake audit before use.
  5. Never overwrite validated artifacts.
  6. Track assumptions until validated or rejected.
  7. Register every experiment with start/end/decision.
  8. Document biases, limitations, and leakage sources explicitly.

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