Clinical Trials Fail Because Standard Monitoring Misses System-Level Signals
Individual organ flags are detected in isolation. Standard safety monitoring catches single-organ adverse events — but misses multi-organ compound signals, the convergence patterns that predict trial failure. Biomarker trajectories, subpopulation responder distributions, and endpoint risk accumulation go undetected until it's too late to intervene. The result: failed trials, sunk costs, and compounds that never reach patients who could benefit.
9-Layer Diagnostic Engine Across Federated Biomedical Knowledge Bases
DiviScan GH-OS queries a federated network of 11,500,000+ biomedical relationships across six authoritative knowledge bases — surfacing system-level convergence patterns that individual organ-based monitoring cannot detect. Six knowledge bases, harmonized at the ontology level, enabling cross-domain pattern recognition across a patient's full biomarker history.
Trial Rescue Value — MIMIC-IV Simulation
| Condition | ICD-10 Code | AUC Score |
|---|---|---|
| Heart Failure | I50· I50.0–I50.9 | 0.890 |
| Acute Kidney Injury | N17· N17.0–N17.9 | 0.887 |
| Type 2 Diabetes Mellitus | E11· E11.0–E11.9 | 0.882 |
| Sepsis | A41· A41.0–A41.9 | 0.869 |
| Anemia | D64· D64.0–D64.9 | 0.815 |
Validated on MIMIC-IV clinical database. Additional phenotypes inferred via federated knowledge graph analysis.
6-Week Shadow Mode Deployment
for your trial or therapeutic area.