The 9-layer GH-OS combines 320 production ML classifiers (AUC > 0.65) with a First Principles Engine that flags internal conflicts — surfacing signals across 17,000+ disease phenotypes and 30M+ biomedical relationships.
Clinical, Pharmaceutical, Population Health — one decision-governance engine.
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. 320 production ML classifiers (AUC > 0.65, n > 100) exist at one layer; the rest are rules, lookups, and structured reasoning. The First Principles Engine sits above all layers — detecting when classifier outputs conflict with knowledge graph inference.
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. 30M+ 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.
320 production ML classifiers (AUC > 0.65, n > 100) trained on MIMIC-IV generate probability scores across conditions spanning critical care, metabolic, cardiovascular, neurological, and renal domains. This is the only trained-ML layer. All other disease associations are inferred via knowledge graph traversal, not trained models. The First Principles Engine cross-validates classifier outputs against KB inference — flagging conflicts where ML probability diverges from biological prior.
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.
Most prediction systems return a score. DiviScan GH-OS goes further — the First Principles Engine cross-validates every ML classifier output against knowledge graph inference. When probability and biological prior diverge, the engine flags the conflict rather than averaging over it.
When a classifier's probability output contradicts the biological inference from the knowledge graph, FPE surfaces the conflict explicitly — rather than silently passing through a potentially wrong signal.
Every conflict between a trained classifier and first-principles inference is logged, timestamped, and included in the intelligence report — with the specific biomarker deviation and ontology path that triggered the flag.
The FPE doesn't hide uncertainty — it quantifies it. The engine distinguishes between high-confidence predictions (ML and KB agree), uncertain predictions (divergence flagged), and knowledge-limited regions (inference only, no classifier available).
The positioning that matters for VP Clinical Development and Head of Biometrics:
A system that knows when to doubt itself is more valuable than one that doesn't. The FPE is the mechanism that makes DiviScan GH-OS audit-ready — every flagged conflict is evidence that the system is reasoning, not just pattern-matching.
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.
Phase II/III trials fail because the wrong patients were enrolled, confounders weren't detected, and responder subpopulations were invisible. DiviScan GH-OS is the engine that surfaces these patterns before a trial is declared a failure.
The engine runs full 9-layer inference across all enrolled subjects, flagging metabolic, pharmacogenomic, and comorbidity confounders that standard DSMB review misses. If a subpopulation is biologically different from the intent-to-treat group, the knowledge graph will surface it.
When a failing Phase III trial has a buried responder subpopulation, standard analysis won't find it. DiviScan's multi-omic inference runs across all enrolled biomarker panels, detecting phenotype-level responder signals invisible to aggregate endpoint analysis.
Endpoint sensitivity modeling across organ-system panels — identifying which endpoints are at statistical risk and which biomarker trajectories predict endpoint miss before data lock. Informed by 30M+ pharmacogenomic and disease relationships.
Alert fatigue is an institutional failure mode. DiviScan GH-OS enters shadow mode first — running in parallel with your existing workflows, surfacing divergences without creating new alert burdens — so you can evaluate before you commit.
DiviScan GH-OS runs silently alongside your existing EHR and EWS systems during the pilot phase — generating reports, flagging divergences, measuring what it would have caught — without touching clinical workflow or creating a single new alert.
NEWS/MEWS sees one patient, one set of vitals, one threshold. The GH-OS sees multi-organ biomarker trajectories, cross-system correlations, and phenotype patterns across the full 9-layer engine — detecting deterioration before it manifests in vitals.
Every signal is traceable to its biomarker source, ontology path, and inference chain. Not a black-box score — an auditable decision governance record that supports clinical review and institutional accountability. The First Principles Engine flags every conflict.
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) + 320 AUC-validated ML classifiers (AUC > 0.65) |
| 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 | 30M+ 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 |
AUC scores are secondary evidence for the 320 production 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: 320 production ML classifiers meet the AUC > 0.65, n > 100 production threshold — validated against MIMIC-IV critical care data spanning critical care, metabolic, cardiovascular, neurological, and renal domains. All other disease associations — spanning 17,000+ phenotypes — are inferred through knowledge graph traversal and LLM reasoning over 30M+ federated biomedical relationships.
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.
Transparency notice: DiviScan GH-OS is not FDA cleared and has not been prospectively validated in live clinical settings. It is not a replacement for clinical judgment. 320 ML classifiers meet the AUC > 0.65, n > 100 production threshold — all other disease associations are inferred via knowledge graph traversal, not trained models. The $134M trial rescue figure is a simulation across a 40-patient MIMIC-IV cohort and does not represent a guaranteed outcome.
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.