Knowledge Graphs for Medical & Clinical Research

Medicine moves at the pace of literature. KnodeGraph reads clinical guidelines, drug monographs, and trial PDFs, then builds a graph of drugs, conditions, interactions, dosages, contraindications, and outcomes. Pharmacy informatics teams and clinical researchers use it to spot drug-interaction risks, map a disease's treatment landscape, or build a trial eligibility graph without bespoke ML engineering.

Why Medical teams use KnodeGraph

  • DrugBank lists 17,000+ approved and experimental drugs with curated interactions — KnodeGraph can ingest the same primary literature DrugBank uses, in your own private graph.
  • The FDA Adverse Event Reporting System (FAERS) gets ~2 million case reports per year. Graph structure is the natural way to spot polypharmacy risk patterns.
  • ClinicalTrials.gov lists 480,000+ studies; eligibility-criteria parsing is a known NLP-hard task that benefits from a guided-extraction template.
  • ICD-11 (WHO, 2022) has 17,000+ diagnostic codes — entity disambiguation matters, and Claude does well at mapping free-text mentions to ICD or SNOMED CT codes when prompted.
  • Pro tier's 50K-node ceiling fits the treatment-graph for a major condition like Type 2 diabetes (~3K drugs, conditions, biomarkers, and outcomes after curation).
  • 100+ language support means non-English regulatory filings (EMA, PMDA, NMPA) join the same graph as US literature.

How the workflow runs

1.Upload guidelines and trial PDFs

Drop in NICE guidelines, ACC/AHA statements, NEJM trial reports, and drug monographs. Use the 'Medical Research' template for entity types.

2.Curate disambiguation

AI may produce both 'metformin' and 'metformin HCl' — merge in the staging UI before they pollute the graph.

3.Filter by edge type

Show only 'interacts_with' edges to audit drug-drug interactions. Show only 'contraindicated_in' to see populations to exclude in a trial.

4.Spot risk clusters

High-degree nodes among interaction edges = polypharmacy hazards. Multiple-edge cycles = potential cascade risks (drug A potentiates drug B which contraindicates condition C).

5.Export for downstream stats

JSON / CSV → R or Python notebook for risk scoring, or feed into a clinical decision-support pipeline.

Why KnodeGraph fits Medical workflows

  • •Templated entity types match clinical taxonomies (drug, dose, route, condition, outcome) — extractions are typed, not loose strings.
  • •Provenance links every claim back to a specific guideline or paper page — auditable for IRB review.
  • •Self-host option keeps PHI inside your hospital network if you have any (de-identified literature usually doesn't).
  • •Faster than building a domain-specific BERT model for relation extraction — Claude handles the ambiguity, you handle the QA.
  • •Cytoscape-based visualisation is conference-poster ready (PNG/SVG export at any zoom).

Frequently Asked Questions

Is KnodeGraph HIPAA-compliant?

The hosted SaaS is not HIPAA-covered today — do not upload PHI (patient identifiers, EHR data) to the standard product. For PHI workflows, the self-hosted plan can be deployed inside a HIPAA-compliant infra you control, with a BAA from Anthropic for the underlying model API. For literature-only research using public papers, the hosted SaaS is fine.

Does it map to standard medical ontologies (SNOMED CT, ICD-11, RxNorm)?

Not natively, but Claude does a passable job inferring codes when prompted in the template. For production-grade mapping, export the graph as JSON and run it through a dedicated terminology service (e.g., NLM's UMLS Metathesaurus). We've seen pharmacy informatics teams adopt that two-step pattern with good results.

How accurate is the extraction for drug-drug interactions?

In our internal pilot using a sample of 50 NEJM and Lancet pieces, Claude correctly identified ~88% of explicitly stated interactions and ~62% of implicit ones (e.g., 'caution in patients on warfarin' as a warfarin-interaction signal). The staging review catches the false positives. We strongly recommend a clinical pharmacist review the graph before any clinical use.

Can I use this for systematic-review screening?

Indirectly, yes. KnodeGraph isn't a Covidence or Rayyan replacement, but it's good at the next step: once you've selected your included studies, KnodeGraph extracts and links the population-intervention-comparison-outcome (PICO) structure across them so synthesis is easier.

What about clinical trial eligibility parsing?

ClinicalTrials.gov criteria are notoriously messy free text. With a 'Trial Eligibility' template, KnodeGraph extracts inclusion/exclusion criteria, drug exposure history, and lab thresholds. Coverage is decent (~75% of criteria captured cleanly in our test) — useful for cohort discovery, not yet for fully automated screening.

Ready to graph your medical work?

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