Synapse MKR - Medical Knowledge Representation

About the Project

Mission: Create a high-quality, explainable knowledge graph of disease-medication relationships for use in GraphRAG-powered medical AI systems where transparency, validation, and consistency are critical. Synapse MKR prioritizes validated knowledge engineering of relationships—the biggest missing piece from biomedical ontologies—starting with disease-medication relationships.

Synapse MKR addresses a fundamental challenge in medical AI: the need for structured, verifiable knowledge rather than opaque neural network predictions. By mapping standardized disease concepts to medications with explicit relationship types, we enable:

Explainable Clinical Decision Support: Every recommendation can be traced to specific knowledge graph edges with citations
Reduced Hallucination Risk: LLMs retrieve from curated relationships rather than generating potentially incorrect information
Regulatory Compliance: Auditable reasoning paths for FDA AI/ML requirements
Interoperability: Based on universal medical terminologies used in EHR systems worldwide

Why Knowledge Graphs for Medical AI?

Traditional large language models (LLMs) face critical limitations in healthcare:

Hallucination of non-existent treatments or contraindications
Inability to cite evidence sources
Expensive retraining for guideline updates
Black-box reasoning incompatible with clinical workflows

GraphRAG (Graph-based Retrieval Augmented Generation) solves these issues by:

Retrieving relevant subgraphs from a validated knowledge base
Providing structured context to LLMs
Enabling citation of specific relationships and evidence sources
Supporting incremental updates without full model retraining

Clinical Applications

Here are a few ways this knowledge graph could power better healthcare.

Patient-Facing

Pre-Visit History Builder

"Remember your medications, rediscover your conditions"

How It Works:

Patient enters their current medications on their smartphone
Using on-device GraphRAG with Synapse MKR, the app performs reverse-lookup to identify likely conditions these medications treat. The AI then asks simple yes/no confirmation questions: "Do you have high blood pressure?" "Do you have diabetes?"
App generates a comprehensive visual summary showing conditions mapped to medications, ready to share with their physician

Privacy First:

All processing happens on-device using local LLMs
No patient data leaves the phone
Complete HIPAA compliance through privacy-by-design

Impact: Patients arrive better prepared. Physicians spend less time on basic history-taking and more time on treatment planning and patient questions. A 15-minute visit becomes more productive.

Graph Value:

Reverse medication-to-condition lookup is only possible with structured knowledge graphs. LLMs alone cannot reliably infer which conditions a patient likely has from their medication list without hallucination risk.

Patient + Physician

Visual Treatment Map

"See how your treatments connect to your health"

Patient View: "My Health Map"

Interactive visual showing patient's conditions with clear lines connecting to each medication. Color-coded by relationship type:

Red: Curative treatments (antibiotics, antivirals)
Blue: Chronic disease management (daily medications)
Green: Prevention medications (aspirin for CVD prevention)
Yellow: Symptom relief (rescue inhalers, pain relievers)

Interaction: Tap a medication → highlights what it treats. Tap a condition → highlights all its treatments.

Physician View: "Clinical Summary Dashboard"

Split-screen visualization showing comprehensive treatment overview:

Left panel: Patient's documented conditions
Right panel: Medications with explicit connections to conditions
Evidence tags: Each connection shows source (AHA/ACC 2017, ADA 2024, etc.)
Smart flags: Medications without mapped conditions (potential gaps or undocumented diagnoses)

Impact: Dramatically reduced cognitive load for physicians reviewing complex medication lists. Patient on 8 medications becomes instantly comprehensible. Faster pattern recognition, easier medication reconciliation, improved patient education.

Graph Value:

Provides explicit, auditable reasoning for each medication-condition link. "Explainable AI" - physicians see WHY connections exist with cited evidence, not black-box predictions. Same graph, two interfaces, different audiences.

Physician-Facing

Multi-Morbidity Safety Check

"Safe prescribing for complex patients"

The Challenge:

65% of Medicare patients have 2+ chronic conditions. Each additional condition exponentially increases drug-disease interaction risk. LLMs struggle with simultaneous multi-disease reasoning.

How It Works:

Before prescribing Drug X for Disease Y, GraphRAG queries patient's complete condition list against Synapse MKR's CONTRAINDICATED_IN relationships:

Patient has: Heart failure, CKD Stage 4, Asthma
Physician considers: Ibuprofen for pain
Graph alerts: Contraindicated in severe HF (worsens fluid retention), contraindicated in CKD Stage 4 (nephrotoxic)
System suggests: Acetaminophen (safe alternative) with dosing considerations

Critical Examples:

NSAIDs in severe heart failure
Metformin in advanced CKD (lactic acidosis risk)
Beta-blockers in severe asthma (bronchoconstriction risk)
ACE inhibitors in pregnancy

Impact: Prevents dangerous drug-disease interactions before they occur. Critical for elderly patients with 4+ conditions where manual checking is error-prone. One prevented adverse event saves thousands in healthcare costs and prevents patient harm.

Graph Value:

Knowledge graphs excel at multi-hop reasoning. Checking contraindications across multiple diseases simultaneously is computationally efficient with graph traversal (milliseconds) versus LLM reasoning (seconds, with hallucination risk).

Patient-Facing

Medication Schedule Optimizer

"Take the right pill at the right time"

The Problem:

Patients on 5+ medications struggle to remember which pills are truly critical versus which have flexible timing. All medications feel equally urgent, leading to either anxiety or poor adherence.

How It Works:

GraphRAG categorizes medications by their relationship type using Synapse MKR:

Highest Priority - TREATS_CURATIVE: Antibiotics, antivirals (must complete course)
High Priority - PREVENTS: Aspirin for MI prevention, vaccines
Medium Priority - MANAGES_CHRONIC: Daily maintenance medications (diabetes, blood pressure)
Lower Priority - TREATS_SYMPTOMATIC: As-needed medications (rescue inhalers, pain relievers)

Smart Features:

Critical reminders for curative medications (never miss doses)
Flexible windows for chronic management medications
As-needed tracking for symptomatic treatments
Visual priority indicators help patients focus on what matters most

Impact: Reduces medication anxiety while improving adherence for critical therapies. Patients understand which medications require strict timing versus which allow flexibility. Studies show relationship-aware reminders improve adherence by 30% compared to generic alarms.

Graph Value:

Relationship types (TREATS_CURATIVE vs MANAGES_CHRONIC) provide inherent prioritization logic. This is native to knowledge graphs but would require complex prompting and fine-tuning with pure LLM approaches.

Physician-Facing

Medication Reconciliation Assistant

"Simplify complex medication lists"

The Challenge:

Patients on 10+ medications often have therapeutic duplication, unnecessary drugs, or orphan medications (no clear indication). Manual reconciliation during hospital admission or care transitions is time-consuming and error-prone.

How It Works:

GraphRAG maps each medication to patient's documented conditions using Synapse MKR, then identifies issues:

Duplicate Therapy Detection:

Example: Patient on both Lisinopril (ACE-I) and Losartan (ARB) for hypertension
Flag: Both treat HTN via similar mechanisms - typically only need one
Action: Suggest consolidation unless specific indication for combination therapy

Orphan Medication Identification:

Example: Patient taking Omeprazole with no documented GERD, gastritis, or peptic ulcer
Flag: Medication without mapped condition in graph
Action: Prompt physician to document indication or consider discontinuation

Unnecessary Chronic Therapy:

Medications started for acute issues but continued indefinitely
Symptomatic medications where underlying condition resolved
Prevention medications where risk factors no longer present

Impact: Average patient on 10+ medications can reduce pill burden by 2-3 medications safely. Lower costs, improved adherence, reduced drug interactions. One study showed 30% reduction in adverse events after systematic medication reconciliation.

Graph Value:

Knowledge graphs excel at finding "what's connected" and "what's missing." Identifying orphan medications (drugs without mapped conditions) is a straightforward graph query but complex for LLMs without structured reasoning.

Key Advantages

🔍 Explainability

Every treatment recommendation includes a traceable path: Disease → [Relationship Type + Evidence] → Medication. Clinicians can audit AI reasoning.

⚖️ Regulatory Compliance

FDA-approved indications only. Explicit contraindication tracking. Version-controlled knowledge base supports reproducibility for regulatory submissions.

🔄 Maintainability

Update 10 edges for new guidelines vs. retraining 70B parameter models. Changes propagate instantly across all queries.

🤝 Interoperability

Built on standardized medical terminologies—the same standards used in EPIC, Cerner, and other major EHR systems.

🛡️ Safety

Automated consistency checks catch contradictions (e.g., drug both treats and contraindicated in same disease). Human review workflow for safety-critical relationships.

📊 Multi-Morbidity Support

Graph traversal handles complex queries: "Find medications for patient with HTN + CKD + Diabetes that don't require dose adjustment."

💰 Cost Efficiency

Graph updates cost <$5K vs. $100K-500K for LLM retraining. Queries run in milliseconds with O(log n) indexed lookups, especially useful in on-premises and low-power devices.

🔬 Research Ready

Enables pharmacovigilance tracking, treatment pattern analysis, gap identification, and hypothesis generation for drug repurposing.

📋 Scalable Validation

Multi-model AI validation pipeline with targeted human expert review enables efficient scaling while maintaining regulatory compliance and clinical accuracy standards.

Current Status & Results (2026-01)

Proof of Feasibility

Initial validation demonstrates the viability of this multi-model LLM methodology for generating high-quality medical knowledge graphs. Preliminary review by a clinical informatics MD/PhD shows accuracy and coverage of granular relationship types and usage estimates substantially exceeding existing open data resources.

Initial Release Progress

230 Disease-medication relationships generated

50 Priority disease concepts covered

Phase 1 Schema design — Complete ✓

Phase 2 Multi-LLM generation — Complete ✓

Phase 3 Automated code validation — In Progress

Disease Coverage

Relationships span high-impact disease categories across organ systems:

Cardiovascular (12 diseases)

HTN, HLD, CAD, HFrEF, HFpEF, AF, Stable angina, DVT/PE, PAD

Endocrine/Metabolic (6 diseases)

T2DM, T1DM, Hypothyroidism, Hyperthyroidism, Osteoporosis, Obesity

Respiratory (5 diseases)

Asthma, COPD, Pneumonia, Acute bronchitis, Allergic rhinitis

Mental Health (6 diseases)

MDD, GAD, Bipolar disorder, ADHD, Insomnia, PTSD

Gastrointestinal (5 diseases)

GERD, IBS, Constipation, Crohn's disease, Ulcerative colitis

Infectious Disease (5 diseases)

UTI, Strep pharyngitis, Influenza, Cellulitis, Acute otitis media

Musculoskeletal (4 diseases)

Osteoarthritis, Rheumatoid arthritis, Gout, Low back pain

Neurologic (4 diseases)

Migraine, Epilepsy, Parkinson's disease, Essential tremor

Other (3 diseases)

CKD, Anemia, BPH

Schema Documentation

Node Types

Disease Node

node_id UUID REQUIRED

Unique identifier for this node instance

snomedct_id String (SCTID) REQUIRED

SNOMED-CT concept identifier

umls_cui String REQUIRED

UMLS Concept Unique Identifier for cross-terminology mapping

preferred_term String REQUIRED

Full clinical name (e.g., "Essential hypertension")

clinical_abbreviation String OPTIONAL

Common abbreviation used in clinical documentation (e.g., "HTN", "T2DM")

semantic_tag String OPTIONAL

SNOMED-CT semantic type (e.g., "disorder", "finding")

Medication Node

node_id UUID REQUIRED

Unique identifier for this node instance

rxcui String (RxNorm RXCUI) REQUIRED

RxNorm concept identifier at ingredient level (IN or MIN for combinations)

umls_cui String REQUIRED

UMLS Concept Unique Identifier

ingredient_name String REQUIRED

Generic drug name (e.g., "Metformin", "Lisinopril")

is_combination Boolean REQUIRED

True if medication contains multiple active ingredients

atc_code String OPTIONAL

WHO Anatomical Therapeutic Chemical classification code

drug_class String OPTIONAL

Therapeutic class (e.g., "ACE Inhibitor", "Biguanide")

mechanism_of_action String OPTIONAL

Brief description of how the drug works

Relationship Types

Primary Treatment Relationships (mutually exclusive)

Type	Definition	Examples
`PREVENTS`	Prophylactic use to prevent disease occurrence	Aspirin for MI prevention, Vaccines for infectious disease
`TREATS_CURATIVE`	Intent is disease resolution or elimination of causative agent	Antibiotics for bacterial infections, Antivirals for acute viral illness
`MANAGES_CHRONIC`	Disease modification or maintenance therapy (no cure expected)	Insulin for diabetes, Statins for hyperlipidemia, ACE-I for hypertension
`TREATS_SYMPTOMATIC`	Symptom palliation without disease modification	NSAIDs for arthritis pain, Antiemetics for nausea

Modifiers (can co-exist with primary relationships)

FIRST_LINE - Guideline-recommended initial therapy
SECOND_LINE - Recommended after first-line failure
ADJUNCTIVE - Used in combination with other therapies

Safety Relationships

CONTRAINDICATED_IN - Absolute contraindication
CAUTION_IN - Relative contraindication or monitoring required
REQUIRES_DOSE_ADJUSTMENT - Dosing modification needed (renal/hepatic/age-based)

Edge Properties

edge_id UUID REQUIRED

Unique identifier for this relationship

relationship_type Enum REQUIRED

One of the relationship types listed above

fda_approved Boolean REQUIRED

Whether this indication is FDA-approved (all edges in current dataset are TRUE)

evidence_source String OPTIONAL

Source of evidence (e.g., "DailyMed", "FDA Label", "AHA/ACC 2017 Guidelines")

evidence_id String OPTIONAL

Identifier for evidence source (e.g., SPL setid, DOI)

estimated_usage_rank Integer OPTIONAL

Estimated clinical usage frequency (1 = highest)

review_status Enum REQUIRED

One of: pending, approved, rejected, needs_revision

mapping_confidence Float (0.0-1.0) OPTIONAL

Confidence score for this relationship

version Integer REQUIRED

Version number (increments on modification)

is_current Boolean REQUIRED

Whether this is the current active version

Output File Formats

CSV Format (for human review & spreadsheet import)

Flat file format suitable for review in Excel/Google Sheets and import into databases.

Example CSV Output:

edge_id,disease_sctid,disease_cui,disease_name,disease_abbrev,med_rxcui,med_cui,med_name,relationship_type,modifiers,fda_approved,evidence_source,estimated_rank,review_status,version,is_current,atc_code,drug_class,moa
550e8400-e29b-41d4-a716-446655440001,38341003,C0020538,Essential hypertension,HTN,29046,C0020649,Lisinopril,MANAGES_CHRONIC,FIRST_LINE,TRUE,AHA/ACC 2017,88,pending,1,TRUE,C09AA03,ACE Inhibitor,ACE inhibition
550e8400-e29b-41d4-a716-446655440002,38341003,C0020538,Essential hypertension,HTN,2599,C0025894,Losartan,MANAGES_CHRONIC,FIRST_LINE,TRUE,AHA/ACC 2017,82,pending,1,TRUE,C09CA01,ARB,Angiotensin II receptor antagonism
550e8400-e29b-41d4-a716-446655440003,38341003,C0020538,Essential hypertension,HTN,17767,C0004147,Amlodipine,MANAGES_CHRONIC,FIRST_LINE,TRUE,AHA/ACC 2017,84,pending,1,TRUE,C08CA01,Dihydropyridine Calcium Channel Blocker,Blocks L-type calcium channels
550e8400-e29b-41d4-a716-446655440159,44054006,C0011860,Type 2 diabetes mellitus,T2DM,6809,C0025598,Metformin,MANAGES_CHRONIC,FIRST_LINE,TRUE,ADA 2024,96,pending,1,TRUE,A10BA02,Biguanide,Decreases hepatic glucose production
550e8400-e29b-41d4-a716-446655440008,55822004,C0020473,Hyperlipidemia,HLD,83367,C0004147,Atorvastatin,MANAGES_CHRONIC,FIRST_LINE,TRUE,ACC/AHA 2018,94,pending,1,TRUE,C10AA05,Statin,HMG-CoA reductase inhibition

JSON-LD Format (for semantic web & graph database import)

Structured Linked Data format following schema.org conventions, suitable for Neo4j, RDF triple stores, and semantic web applications.

Example JSON-LD Output:

{
  "@context": "http://schema.org/",
  "@graph": [
    {
      "@type": "MedicalCondition",
      "@id": "snomed:38341003",
      "identifier": "38341003",
      "sameAs": "umls:C0020538",
      "name": "Essential hypertension",
      "alternateName": "HTN",
      "codeValue": "C0020538",
      "codingSystem": "UMLS"
    },
    {
      "@type": "Drug",
      "@id": "rxnorm:29046",
      "identifier": "29046",
      "sameAs": "umls:C0065374",
      "name": "Lisinopril",
      "drugClass": "ACE Inhibitor",
      "mechanismOfAction": "Angiotensin-converting enzyme inhibition",
      "code": {
        "@type": "MedicalCode",
        "codeValue": "C09AA03",
        "codingSystem": "ATC"
      }
    },
    {
      "@type": "TherapeuticRelationship",
      "@id": "uuid:550e8400-e29b-41d4-a716-446655440000",
      "source": "snomed:38341003",
      "target": "rxnorm:29046",
      "relationshipType": "MANAGES_CHRONIC",
      "modifier": ["FIRST_LINE"],
      "evidenceSource": "AHA/ACC 2017",
      "evidenceLevel": "A",
      "usageRank": 1,
      "reviewStatus": "approved",
      "version": 1,
      "isCurrent": true,
      "dateCreated": "2026-02-03T00:00:00Z"
    }
  ]
}

Cypher Import Script (for Neo4j)

Direct graph database loading script for Neo4j or compatible graph databases.

Example Cypher Script:

// Create Disease Node
CREATE (d:Disease {
  node_id: '550e8400-e29b-41d4-a716-446655440100',
  snomedct_id: '38341003',
  umls_cui: 'C0020538',
  preferred_term: 'Essential hypertension',
  clinical_abbreviation: 'HTN',
  is_active: true,
  snomed_version: '2025-01',
  last_updated: datetime()
})

// Create Medication Node
CREATE (m:Medication {
  node_id: '550e8400-e29b-41d4-a716-446655440200',
  rxcui: '29046',
  umls_cui: 'C0065374',
  ingredient_name: 'Lisinopril',
  is_combination: false,
  atc_code: 'C09AA03',
  drug_class: 'ACE Inhibitor',
  mechanism_of_action: 'Angiotensin-converting enzyme inhibition',
  rxnorm_version: '2025-01',
  is_active: true
})

// Create Relationship
CREATE (d)-[r:MANAGES_CHRONIC {
  edge_id: '550e8400-e29b-41d4-a716-446655440000',
  modifiers: ['FIRST_LINE'],
  fda_approved: true,
  evidence_source: 'AHA/ACC 2017',
  estimated_usage_rank: 1,
  review_status: 'approved',
  mapping_confidence: 0.98,
  version: 1,
  is_current: true,
  created_date: datetime()
}]->(m)

// Create Indexes
CREATE INDEX ON :Disease(snomedct_id);
CREATE INDEX ON :Disease(umls_cui);
CREATE INDEX ON :Disease(clinical_abbreviation);
CREATE INDEX ON :Medication(rxcui);
CREATE INDEX ON :Medication(umls_cui);

Methodology

We will employ a multi-model LLM approach with human oversight to ensure accuracy and clinical validity. The Synapse MKR methodology is designed to scale efficiently while maintaining high quality through automated validation and targeted expert review.

Development Phases

Phase 1: Schema Design
Development of comprehensive knowledge graph schema optimized for disease-medication relationships. Defines node types (Disease, Medication), relationship types (TREATS_CURATIVE, PREVENTS, MANAGES_CHRONIC, TREATS_SYMPTOMATIC, CONTRAINDICATED_IN, REQUIRES_DOSE_ADJUSTMENT), and metadata fields aligned with standardized medical terminologies.
Phase 2: Multi-Model LLM Generation
Three-model validation pipeline for initial relationship generation:

• Model 1 (Claude Sonnet 4.5 Thinking): Primary relationship generation with focus on clinical accuracy and evidence-based medicine. Generates candidate disease-medication pairs with relationship types, modifiers, and clinical rationale.

• Model 2 (Gemini 3 Pro): Independent cross-validation and correction. Validates relationship types, identifies missing critical medications, flags potential contraindications, and corrects errors from Model 1.

• Model 3 (GPT 5.2 Thinking): Final arbitration and enrichment. Reconciles disagreements between Models 1 and 2, adds missing metadata (ATC codes, drug classes, mechanisms of action), and assigns confidence scores based on consensus.

Automated Consistency Checks: Validation rules ensure no duplicate relationships, verify relationship hierarchy logic, check contraindication structure, and identify orphaned nodes.

Quality Assurance Criteria: All relationships must trace to FDA labeling or major clinical guidelines (AHA/ACC, IDSA, ADA, GINA, GOLD, etc.). Relationships require agreement from ≥2 of 3 LLMs or explicit human override. Safety-critical contraindications flagged for mandatory expert review.
Phase 3: Automated Code Validation
LLM-based validation of medical codes (SNOMED-CT, RxNorm, UMLS CUIs, ATC codes) grounded in authoritative vocabularies. Automated verification ensures code-concept alignment and corrects common mapping errors.
Phase 4: Automated Relationship Validation
Multi-LLM validation and discovery of relationships grounded in vetted medical sources and vocabularies. Evidence sources include:

• MED-RT

• DailyMed (FDA-approved drug labeling)

• Drugs@FDA (FDA approval database)

• PubChem (NIH chemical database)

• PubMed Central open-access subset

• ClinicalTrials.gov

• Major society clinical guidelines (AHA/ACC, IDSA, ADA, etc.)

• UMLS knowledge sources and vocabularies
Phase 5: Human Domain Expert Validation
Clinical specialists from respective domains review and validate relationships within their expertise. Experts adjudicate flagged conflicts, validate safety-critical contraindications, and approve final relationship set for their specialty area.
Phase 6: Methodology Scaling
Expansion of validated methodology to increase coverage of current relationship type (disease-medication) and extension to additional clinical relationship types (drug-drug interactions, disease-disease comorbidities, medication-lab test relationships, etc.).
Phase 7: Clinical Validation Studies
Real-world validation of knowledge representation in GraphRAG systems. Clinical utility studies measuring impact on diagnostic accuracy, treatment appropriateness, and clinical decision support performance.

Current Limitations and Disclaimer

Current Limitations

Development Status:

Early Stage Dataset: This is an initial release focusing on high-impact, high-prevalence conditions. Coverage of the full medical knowledge domain is incomplete and will expand in future releases.
Prioritized Coverage: Disease-medication relationships have been selected based on clinical prevalence and impact. Rare conditions, specialty medications, and comprehensive drug-drug interactions are not yet included.
Limited Expert Validation: While this dataset has undergone multi-model AI validation and initial clinical review by an MD/PhD clinical informaticist, it has not been comprehensively vetted by a panel of domain experts across all specialties.

Known Gaps:

Pediatric and geriatric-specific dosing relationships
Comprehensive contraindications beyond the most critical
Off-label uses (intentionally excluded)
Drug-drug interactions
Genetic/pharmacogenomic considerations
Complete coverage of all therapeutic alternatives

Medical Disclaimer

⚠️ NOT FOR CLINICAL USE

This dataset is provided for research, education, and development purposes only. It is explicitly NOT intended for clinical decision-making, patient care, or any medical treatment decisions.

Requirements for Any Use:

Any implementation in clinical, research, or commercial settings MUST undergo independent validation by qualified healthcare professionals
Users MUST comply with all applicable regulations including FDA guidance on Clinical Decision Support Software, EU AI Act, and local regulatory requirements
This dataset does not constitute medical advice and should not be used as a substitute for professional medical judgment

No Warranty

The dataset is provided "AS IS" without warranty of any kind, express or implied, including but not limited to warranties of accuracy, completeness, merchantability, or fitness for a particular purpose.

Limitation of Liability

The creators, contributors, and distributors of this dataset shall not be liable for any direct, indirect, incidental, special, consequential, or exemplary damages arising from the use of this data, including but not limited to medical errors, patient harm, or regulatory non-compliance.

User Responsibility

By accessing or using this dataset, you acknowledge that:

You are responsible for validating all information before any clinical, research, or commercial application
You will not use this data for patient care without appropriate regulatory approval and clinical validation
You understand that medical knowledge evolves and relationships in this dataset may become outdated
You will implement appropriate safeguards and human oversight for any AI system using this data

Regulatory Compliance

Users must ensure compliance with:

FDA 21 CFR Part 11 (if used in FDA-regulated contexts)
HIPAA and patient privacy regulations (if integrated with patient data)
EU AI Act requirements for high-risk AI systems
Local medical device and clinical decision support regulations
Institutional review board (IRB) approval for research use

For questions about appropriate use, validation requirements, or regulatory compliance, please consult qualified legal counsel and clinical experts in your jurisdiction.

About the Project

Why Knowledge Graphs for Medical AI?

Clinical Applications

Pre-Visit History Builder

How It Works:

Privacy First:

Graph Value:

Visual Treatment Map

Patient View: "My Health Map"

Physician View: "Clinical Summary Dashboard"

Graph Value:

Multi-Morbidity Safety Check

The Challenge:

How It Works:

Critical Examples:

Graph Value:

Medication Schedule Optimizer

The Problem:

How It Works:

Smart Features:

Graph Value:

Medication Reconciliation Assistant

The Challenge:

How It Works:

Duplicate Therapy Detection:

Orphan Medication Identification:

Unnecessary Chronic Therapy:

Graph Value:

Key Advantages

🔍 Explainability

⚖️ Regulatory Compliance

🔄 Maintainability

🤝 Interoperability

🛡️ Safety

📊 Multi-Morbidity Support

💰 Cost Efficiency

🔬 Research Ready

📋 Scalable Validation

Current Status & Results (2026-01)

Proof of Feasibility

Initial Release Progress

Disease Coverage

Cardiovascular (12 diseases)

Endocrine/Metabolic (6 diseases)

Respiratory (5 diseases)

Mental Health (6 diseases)

Gastrointestinal (5 diseases)

Infectious Disease (5 diseases)

Musculoskeletal (4 diseases)

Neurologic (4 diseases)

Other (3 diseases)

Schema Documentation

Node Types

Disease Node

Medication Node

Relationship Types

Primary Treatment Relationships (mutually exclusive)

Modifiers (can co-exist with primary relationships)

Safety Relationships

Edge Properties

Output File Formats

CSV Format (for human review & spreadsheet import)

Example CSV Output:

JSON-LD Format (for semantic web & graph database import)

Example JSON-LD Output:

Cypher Import Script (for Neo4j)

Example Cypher Script:

Methodology

Development Phases

Current Limitations and Disclaimer

Current Limitations

Medical Disclaimer

No Warranty

Limitation of Liability

User Responsibility

Regulatory Compliance

Download Initial Release

Phase 2 Results Available

⚠️ Download Agreement Required

License