Clinical
Risk Engine
Calibrated malignancy risk scoring for biopsy triage — designed for the clinician, not around them.
01 · Problem
A probability isn't a decision.
Pathologists triage biopsy cases under heavy cognitive load. A raw model probability — even a confident one — doesn't tell them when the model is uncertain.
The cases that matter most are the ambiguous ones, sitting on either side of the decision boundary. Those need a flag, not just a number.
High cognitive load
Mis-triage is expensive
Ambiguity needs a flag
02 · Solution architecture
Calibration is a first-class layer.
Raw ensemble probability isn't a clinical signal. Calibration makes the number actionable.
The spine
FNA vector
30 features
Validation
schema + range
Ensemble
GBM + RF voting
Calibration
isotonic + Wald CI
FNA vector
30 cell-nucleus features per biopsy slide.
Validation
Schema enforcement and range checks before inference.
Ensemble
Gradient boosting + random forest voting, SHAP attribution.
Calibration
Isotonic calibration over the WDBC training cohort. The ambiguity flag fires downstream when the 90% CI straddles 0.5.
03 · Live triage
One case at a time.
Pick a case, see the calibrated probability, watch the ambiguity flag fire when the model is unsure.
The case
+5 more features applied silently
The inference
p(malignancy)
0.02
CI 0.01 – 0.03
The triage
Standard review queue
Cohort position · 4th %ile
1. Case — Pick a case — the surrogate loads its biopsy feature vector.
Interactive prototype · calibrated surrogate of the production ensemble.
04 · Impact
From probability to decision.
A calibrated probability with an ambiguity flag turns a raw model output into something a clinical workflow can route on.
0.99
AUC (calibrated ensemble)
0.041
Brier loss post-calibration
22ms
per-case inference latency
05 · What's next
From a triage signal to a learning loop.
The system improves with every clinician decision it observes.
FHIR ingestion
Pull FNA observations from PACS/LIS directly.
Drift monitoring
Watch input distributions, auto-flag calibration drift.
Human-in-the-loop
Clinician overrides feed the calibration retrain queue.
Triage that knows what it doesn't know.
Happy to walk through the calibration approach, the ambiguity policy, and what FHIR integration would look like.