The 9-layer GH-OS detects associations across 17,000+ disease phenotypes through federated knowledge graph inference and LLM reasoning. Fifteen MIMIC-IV classifiers are one layer of nine.
Designed to reduce alert fatigue — not amplify it.
Not a scoring model. DiviScan GH-OS is a decision-governance engine — layering biomarker ingestion, knowledge graph inference, ontology harmonization, and utility-weighted decision logic. Fifteen trained classifiers exist at one layer; the rest are rules, lookups, and structured reasoning.
Multi-source lab and vitals intake with normalization across organ-system panels. Structured for downstream multi-omic inference.
Organ-system panel decomposition — hepatic, renal, cardiac, metabolic, hematologic — with cross-panel correlation mapping.
Parallel federated lookup across HPO, PrimeKG, Hetionet, PharmGKB, ClinVar, and DisGeNET. 11M+ relationships queried for differential enrichment.
Translates abnormal biomarker deviations into standardized phenotype terms across nine integrated ontology sub-layers, resolving cross-ontology conflicts into a unified inference representation.
Fifteen MIMIC-IV-trained classifiers generate probability scores across conditions including Hepatic Failure, DIC, Acute MI, Cardiac Arrest, CKD, ARDS, Respiratory Failure, Heart Failure, Ischemic Stroke, Type 2 Diabetes, AKI, Sepsis, Electrolyte Disorders, Anemia, and Epilepsy/Seizures. This is the only trained-ML layer. All other disease associations are inferred via knowledge graph traversal, not trained models.
Proprietary decision layer applying utility-weighted thresholds — not raw probability cutoffs. Context-sensitive alerting designed to reduce alert fatigue by filtering clinically non-actionable signals.
Integrates signals across organ systems to detect multi-system deterioration patterns invisible to single-variable monitors or single-disease models.
Composite acuity scoring combining multi-omic inference outputs, utility gates, and temporal biomarker trajectories into actionable risk tiers across all three operational domains.
Structured output with organ-level breakdowns, confidence intervals, phenotype-gene evidence trails, cross-ontology reasoning chains, and domain-specific reporting for clinical, pharma, and population health consumers.
Decision-governance across clinical, pharmaceutical, and population domains — delivering clinically actionable signals at each layer of the health infrastructure.
Move beyond NEWS/MEWS. The GH-OS processes routine lab panels and vitals through its 9-layer engine to surface multi-organ deterioration patterns before they become critical events.
Identify why trials fail before they fail. The simulation engine models biomarker trajectories across trial arms, detecting subpopulation signals and endpoint risks that traditional DSMB reviews miss.
Population-scale knowledge graph inference without centralizing sensitive data. Federated architecture enables cross-institutional pattern detection across distributed health systems.
Traditional early warning systems are single-domain scoring tools. EHR-embedded models apply raw probability thresholds. DiviScan GH-OS operates as a federated decision-governance engine spanning all three health domains simultaneously.
| Capability | Traditional EWS | EHR-Embedded | DiviScan GH-OS |
|---|---|---|---|
| Input Variables | 6–7 vital signs | EHR data fields | Dozens of organ-system endpoints + federated KB |
| Scoring Method | Aggregate threshold | Single-model probability | Utility-weighted decision governance |
| Knowledge Bases | None | None | HPO, PrimeKG, Hetionet, PharmGKB, ClinVar, DisGeNET |
| Operational Domains | Clinical only | Clinical only | Clinical + Pharmaceutical + Population Health |
| Disease Coverage | General deterioration | Single condition | 17,000+ disease phenotypes (knowledge graph inference) + 15 AUC-validated ML classifiers |
| Alert Logic | Fixed threshold | Probability cutoff | Utility Gate (designed to reduce alert fatigue) |
| Federated Learning | No | No | Population-scale federated inference |
| Phenotype Inference | No | No | 11M+ cross-ontology relationships |
Signals are evaluated against three conditions before clinical deployment. Raw accuracy metrics alone do not determine clinical utility.
Does the signal match known clinical truth? The output must be clinically coherent with established disease pathophysiology — not just statistically correlated.
Does it surface signals beyond what standard alerting would catch? Utility requires detecting patterns invisible to NEWS/MEWS or single-variable monitors.
Is the evidence trail interpretable by the clinician? Signals must be traceable to specific biomarker deviations and ontology relationships — not a black-box score.
| Mode | Optimization Target | Key Metric |
|---|---|---|
| Hospital | Minimize alert fatigue, maximize actionability | Sensitivity + Utility Gate pass rate |
| Pharma | Subpopulation enrichment + endpoint risk detection | Specificity + responder signal strength |
| Government | Cross-institutional coverage + population signal breadth | Coverage depth + phenotype frequency |
Transparency is a design principle, not an afterthought.
| Claim | Status |
|---|---|
| 17,000+ disease associations via knowledge graph inference | YES |
| 15 diseases with AUC-validated ML scores (MIMIC-IV) | YES |
| 9-layer decision-governance engine is operational | YES |
| Provisional patent on Utility Gate methodology | YES |
| MIMIC-IV retrospective validation (15 diseases) | YES |
| LLM reasoning over biomedical ontologies for >17K diseases | YES |
Disclaimers: DiviScan GH-OS is not FDA cleared, has not been prospectively validated in live clinical settings, is not a replacement for clinical judgment, and not all disease associations are backed by trained ML — many are inferred via knowledge graph traversal.
AUC scores are secondary evidence for the 15 ML classifiers. Clinical utility is evaluated via the three-condition framework above. Transparent about stage — these are demonstration-level results with clear biological signal.
Scope: 15 diseases have AUC-validated ML scores (Hepatic Failure, DIC, Acute MI, Cardiac Arrest, CKD, ARDS, Respiratory Failure, Heart Failure, Ischemic Stroke, Type 2 Diabetes, AKI, Sepsis, Electrolyte Disorders, Anemia, Epilepsy/Seizures). All other disease associations — spanning 17,000+ phenotypes — are inferred through knowledge graph traversal and LLM reasoning over federated biomedical ontologies.
DiviScan GH-OS utility-weighted inference methodology is protected under a provisional patent. The core innovation — Utility Gate scoring applied to federated multi-knowledge-base biological inference — is proprietary to RESSS Global Holdings LLC.
DiviScan is operational software with demonstrated capabilities, validated against MIMIC-IV critical care data. We are seeking enterprise and institutional partners for prospective validation.
Whether you're evaluating trial rescue intelligence, clinical deterioration detection, or population health infrastructure — we'd like to show you what DiviScan GH-OS can do with your data.
We'll review your submission and respond within 2 business days.