cancer-knowledge-base

ranjithraj/cancer-knowledge-base

cancer-knowledge-base is a machine learning model from ranjithraj. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as cc-by-4.0.

Educational knowledge base on cancer types, classification, case studies, treatment regimens, monitoring, clinical trials, and guidelines.

Downloads · 30 days

Access

Public

Updated Aug 11, 2026

Repo size

5.3 MB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.jsonl8.9 MB · 63%

At a glance

License: cc-by-4.0
Access: Public
Created: Aug 11, 2026
Updated: Aug 11, 2026
SHA: 5d0cc75a

Spaces

Model card

From the Hugging Face model README

Cancer Knowledge Base

Educational knowledge base on cancer types, classification, case studies, treatment regimens, monitoring, clinical trials, and guidelines.

Contents (parquet files)

File	Rows	Description
`documents.parquet`	57	De-identified documents: metadata + body text
`sections.parquet`	396	Markdown headings + verbatim section content
`markdown_tables.parquet`	209	All markdown tables (header/rows as JSON)
`markdown_links.parquet`	145	Internal links between documents
`prognosis.parquet`	168	Lethality/survival tables by cancer type and stage (dated, educational)
`biomarkers.parquet`	72	Mutations, receptors, genetic syndromes
`guidelines.parquet`	142	Guideline-grade treatment statements per cancer (NCCN/ESMO/ASCO)
`subtypes.parquet`	110	Histologic subtypes by cancer
`drugs.parquet`	59	Drugs, class, mechanism, toxicity
`staging.parquet`	31	Staging systems (WHO, BCLC, Gleason, NCCN, Clark, Breslow)
`regimens.parquet`	85	Treatment regimens per case and phase
`monitoring.parquet`	109	Factors to monitor during chemo/targeted/surgery/RT
`diagnostics.parquet`	96	Recommended diagnostics per case
`labs.parquet`	72	Baseline labs (synthetic, clearly flagged)
`imaging_findings.parquet`	59	PET-CT / imaging findings (synthetic, clearly flagged)
`consultations.parquet`	59	Consultation plan evolution (synthetic, clearly flagged)
`case_facts.parquet`	218	Structured case fields (diagnosis, staging, prognosis)
`evidence_levels.parquet`	101	Evidence grading (guidelines + trials) per entity
`codes.parquet`	82	Interop codes: ICD-10 for sites/types, HGNC for biomarkers, LOINC for labs, ICD-O-3 for breast subtypes
`regimen_drugs.parquet`	100	Regimen-to-drug join table
`mcq_questions.parquet`	124	Oncology multiple-choice eval questions with rationale (112 total)
`prognosis_sources.parquet`	32	Prognosis rows linked to verified SEER references (vintage + value)
`locales.parquet`	9	Registered target languages (BCP-47 codes, review requirement, priority)
`glossary.parquet`	421	Code-anchored localization terminology registry: concept -> code (ICD/LOINC/HGNC/ICD-O-3) -> local term per locale
`translations.parquet`	4	Per-document localized bodies with master_hash/content_hash provenance and review status

RAG Benchmark

benchmark/ contains an oncology RAG evaluation suite built from this knowledge base:

retrieval_pool.parquet — ~1,092 chunked retrieval units (sections, markdown tables, domain tables)
mcq_benchmark.parquet — 112 multiple-choice questions with golden docs, difficulty, evidence level, and citations
mcq_robustness.parquet — 336 MedRGB-style variants (sufficiency / noise / counterfactual-robustness)
mirage_format.jsonl / medrgb_format.jsonl — interchange exports for MIRAGE-family leaderboards and MedRGB harnesses (see benchmark/MIRAGE_INTEGRATION.md)
leaderboard.json — recorded system results

Evaluate your own system with scripts/eval_mcq.py (BM25 or embedding retrieval, or score your predictions).

Ground-truth validation scorecards

scorecards/ holds the results of screening LLM-synthetic oncology datasets against the KB's cited statements (scripts/validate_synthetic.py). Each claim is tiered supported / partial / unsupported by lexical coverage of site-gated evidence, and every item links to the matching evidence chunk.

cancerguide.all.summary.md + .scorecard.json + .claims.parquet — CancerGUIDE (316 synthetic patient notes): 238 scorable claims, 87.0% supported, 12.6% partial, 0.4% unsupported.

Localization (l10n)

The KB is being localized with provenance — every translation keeps its citation (via the master document), a master_hash so a change in the English source flags the locale stale, and an explicit review status (auto → reviewed → approved).

locales.parquet — registered languages (de, zh, es, hi + pipeline candidates)
glossary.parquet — code-anchored terminology registry: each concept is linked to its language-neutral code (ICD-10 / ICD-O-3 / LOINC / HGNC) and maps to the local clinical term, so structured rows localize for free while codes stay the join key across languages.
translations.parquet — localized document bodies (pilot: know/breast-ref.md at 100% translatable coverage in de/zh/es/hi) with master_hash/content_hash and status.

Coverage is not clinical approval: status='auto' rows are glossary-generated drafts that must be clinically reviewed (reviewed) before use. See l10n-moat.md and scripts/localize_pilot.py --coverage.

Privacy

This is the de-identified public subset. The real patient case (cases/specific/ — PHI) and all content derived from it are excluded. Documents are filtered by phi = 0.

Synthetic rows in labs, imaging_findings, and consultations are clearly flagged with synthetic = 1 and are illustrative public-case data — they are not real patient values.

Disclaimer

Educational reference only — not medical advice. Survival figures are population averages and may be outdated (prognosis.as_of marks their review date); consult current guidelines and clinicians for patient decisions.

License: cc-by-4.0
Languages: en
Created: Aug 11, 2026
Updated: Aug 11, 2026