Skip to main content

SepsiGuard AI

See Sepsis Before It Strikes

Detect early sepsis risk in real-time and prioritize critical ICU patients instantly.

Overview

SepsiGuard AI is a real-time clinical analytics platform that continuously monitors ICU patient data to detect early indicators of sepsis and septic shock. Built on Snowflake with Cortex AI, it ingests live vital signs, lab results, clinical notes, and device telemetry. Using machine learning and GenAI-driven feature extraction, the system calculates a sepsis risk score for each patient and visualizes it on a real-time dashboard.

Rather than replacing clinical judgment, SepsiGuard AI augments decision-making by highlighting high-risk patients early, allowing doctors to intervene sooner and prioritize care where it matters most.

Challenges

  • Delayed Detection: Early sepsis symptoms are subtle and distributed across multiple data sources.
  • Alarm Fatigue: ICUs generate excessive alerts, causing clinicians to ignore critical warnings.
  • Data Fragmentation: ICU data resides across monitors, EMRs, lab systems, and notes.
  • Prioritisation Under Pressure: Clinicians must quickly decide which patient needs attention first.

Solution

  • AI continuously correlates vitals, labs, and trends to identify early deterioration patterns that humans may miss.
  • Risk-based prioritization reduces noise by escalating only clinically meaningful alerts.
  • Snowflake unifies all real-time and historical patient data into a single analytics layer.
  • The dashboard ranks patients by sepsis risk, enabling rapid, informed triage.

Key Features

1

Real-Time Sepsis Risk Scoring

Continuously computes patient-level risk scores using vitals (HR, BP, RR, SpO₂), labs (lactate, WBC), trends and clinical signals.
2

Live ICU Prioritisation Dashboard

A visual dashboard showing all ICU patients ranked by deterioration risk, color-coded for urgency.
3

Early Warning Alerts

Sends alerts to clinicians when a patient crosses predefined risk thresholds — before septic shock onset.
4

Explainable AI Indicators

Shows contributing factors (e.g. rising lactate, dropping BP, tachycardia) so clinicians understand why a patient is flagged.
5

Historical & Trend Analysis

Displays time-series trends for each patient, enabling clinicians to see deterioration patterns at a glance.
6

Secure Clinical Architecture

Backend powered by FastAPI, frontend in React, with secure, compliant data handling on Snowflake.

How it works?

Live Data Ingestion

Continuous ingestion of ICU vitals, lab feeds, and clinical observations into Snowflake.

Feature Engineering & Signal Detection

Cortex AI models analyze trends, deltas, and combinations of signals associated with early sepsis.

Risk Prediction

Machine learning models compute real-time sepsis and septic shock risk scores for each patient.

Dashboard Visualisation

The React-based dashboard displays patient risk rankings, trends, and alerts in real time.

Clinical Action

Doctors and nurses use the dashboard to prioritize assessments, interventions, and escalation.

Continuous Learning

Models improve over time as more ICU cases and outcomes are observed.

How is SepsiGuard AI used?

An ICU team monitors 20 patients during a busy night shift. SepsiGuard AI flags two patients whose vitals and labs show early sepsis patterns — even though both appear clinically stable. The dashboard ranks them at the top of the risk list. Doctors intervene early with antibiotics and fluids, preventing progression to septic shock and avoiding emergency escalation hours later.

Why is sepsis detected too late?

Septic shock is one of the leading causes of mortality in ICUs worldwide. The biggest challenge is time — clinical deterioration can happen rapidly, and early signs of sepsis are subtle, scattered across vitals, labs, and clinician notes. Doctors and nurses are often overwhelmed with alarms and dashboards that react only after a patient deteriorates, rather than predicting deterioration early. Manual monitoring makes it difficult to prioritize which patients need immediate attention, increasing mortality risk and ICU burden.

What impact does it deliver?

  • Earlier Intervention: Detects sepsis hours earlier than traditional rule-based alerts.
  • Improved Prioritisation: Helps clinicians focus attention on the sickest patients first.
  • Reduced ICU Mortality Risk: Early detection is strongly correlated with better outcomes.
  • Operational Efficiency: Doctors can manage more patients safely with clearer prioritization.
  • Scalable Intelligence: One dashboard can support multiple ICUs simultaneously.