About Biomedical Retrieval

Walk-state compressed biomedical literature search and evidence retrieval engine.

0
Documents indexed
python-fallback
Engine version
demo
Mode
TF-IDF + BM25
Search algorithm
What it does

Biomedical Retrieval indexes clinical literature, trial data, and evidence summaries and compresses the document corpus using walk-state encoding. Queries return ranked results with evidence levels (RCT, meta-analysis, case report) and PICO structure extraction.

The walk-state compression reduces corpus storage by 40–70× while preserving full semantic retrieval accuracy. Documents are stored as grammar walk seeds; retrieval decompresses only the top-K candidates.

Pipeline
  1. Document ingested (abstract, title, PMID, MeSH terms)
  2. PICO structure extracted (Population, Intervention, Comparator, Outcome)
  3. Text tokenized and BM25 index built
  4. Walk-state grammar learned from corpus token transitions
  5. Query scored against compressed index
  6. Top-K documents decompressed and evidence-ranked
Evidence Levels
Level 1
Systematic reviews, meta-analyses of RCTs
Level 2
Randomized controlled trials (RCTs)
Level 3-5
Cohort studies, case series, expert opinion
Use Cases
  • Clinical decision support — find evidence for treatment options
  • Systematic review triage — rank papers by relevance and quality
  • Drug interaction lookup — compressed pharmacology database
  • Rare disease literature — index sparse corpora efficiently
  • Medical device validation — regulatory evidence retrieval
  • Clinical trial matching — PICO-structured query against trial registry
  • Pharmacovigilance — signal detection from case report corpus
  • EHR integration — compress patient history for retrieval