TIER 1Critical-care telemetry — research prototype

Pulsemind

An asynchronous ICU telemetry processor. An XGBoost classifier scores incoming stream events and gates higher-cost LLM rationalisation calls so they fire only on detected anomalies.

Provenance

Role
Team — full-stack AI engineer
Period
May 2026 — ongoing
Stack
Python · XGBoost · PyTorch · scikit-learn · React

Measured

Classifier forward pass
<5 msper stream event

Architecture Flow

Pulsemind telemetry risk pipelineFive-stage pipeline: telemetry ingest over an mTLS WebSocket → risk classifier (XGBoost, under 5ms per event) → decision gate (anomaly detected) → LLM rationalisation, on the anomaly path only, roughly 15 seconds → structured rationale outputINGEST LAYERPHYSIOLOGYmTLS WebSocketRISK CLASSIFIERXGBOOST<5ms / eventDECISION GATEANOMALY?no → stop hereLLM RATIONALEANOMALY PATH ONLY~15 sRATIONALEOUTPUTstructuredFAST PATH — EVERY EVENTCONDITIONAL LLM GATINGSLOW PATH — ANOMALIES ONLY
PULSEMIND — TELEMETRY RISK PIPELINE

Architecture Pattern

Asynchronous inference pipeline with conditional explainability gating

Stream events are scored by an XGBoost classifier, selected in a bake-off against LightGBM and CatBoost, trained on the credentialed MIMIC-IV (PhysioNet) de-identified ICU dataset under its data use agreement; demonstrations run on a synthetic derivative built from that dataset. The feature set is 109 columns: eleven frozen ventilator time-series parameters — PEEP, PIP and FiO2 among them — each expanded into observation, staleness and imputation columns, plus static comorbidity, demographic and drug-exposure features. In the serving demo, telemetry reaches the classifier over an mTLS WebSocket. Events scoring below the risk threshold complete on the fast path alone; only those flagged as anomalous trigger the downstream LLM rationalisation call, the expensive step the gate exists to avoid paying for. No identifiable patient data is used, and there is no clinical deployment.