Home▸AI Triage & Virtual Patient Chatbot▸AI Symptom Checker Triage Recommendation Simulator

🤖 AI Symptom Checker Triage Recommendation Simulator

A chatbot symptom checker that provides recommendations for home care versus seeking medical attention based on the patient's reported symptoms.

AI Triage & Virtual Patient Chatbot2DModerate60 FPS
ai-symptom-checker-triage-recommendation-simulator ↗ Open standalone

Symptom Entry — Where Triage Begins

A short chat message launches the entire triage pipeline.

  • 1 in 5: Avg symptom checkers used (US adults yearly)
  • 12 words: Median input length (first complaint message)
  • ~70-80%: Free-text NLP accuracy (symptom extraction)
  • Headache: Common entry symptom (top reported complaint)

Free-text vs structured intake

Chatbots parse loose language into structured symptom tags.

Entity extraction

NLP model tags body location, symptom type, and modifiers.

Ambiguity handling

Vague phrases trigger a clarifying question immediately.

Follow-Up Questions — Hunting for Red Flags

The AI asks targeted questions to narrow the diagnosis space.

  • 5-8: Avg follow-up questions (per triage session)
  • 12+: Red flag question types (chest pain, breathing, etc)
  • ~65%: Completion rate (finish full questionnaire)
  • 3-5 min: Time per session (typical chat duration)

Severity probing

Pain scales and duration questions quantify the complaint.

Red flag checklist

Fixed questions screen for danger signs every time.

Branching logic

Each answer changes which question comes next.

Symptom Pattern Matching

Collected answers are compared to a library of known symptom clusters.

  • 800+: Symptom pattern library (condition templates)
  • Bayesian: Matching approach (+ ML classifiers)
  • ~80%: Top-3 accuracy (correct condition in top 3)
  • Critical: False negative concern (missed emergencies)

Pattern libraries

Curated symptom-condition maps built from clinical data.

Probabilistic scoring

Each candidate condition gets a likelihood score.

Safety-first bias

Ties are broken toward the more urgent outcome.

Urgency Classification

Matched patterns are converted into a single urgency score.

  • 3: Urgency tiers (home, doctor, emergency)
  • 0-100: Scoring scale (composite urgency score)
  • High: Red flag weight (dominates final score)
  • ~70: Escalation threshold (triggers emergency tier)

Composite scoring

Severity, duration, and red flags combine into one score.

Threshold bands

Score ranges map directly to the three care tiers.

Override rules

Any single red flag can force emergency regardless of score.

Care Recommendation Delivered

The patient receives one clear, actionable triage outcome.

  • 3: Outcome options (home / doctor / ER)
  • ~60%: User trust in verdict (follow the recommendation)
  • Disclaimer: Liability safeguard (not a diagnosis)
  • Always: Re-check prompt (worsening symptoms guidance)

Clear verdict card

One prominent card states the recommended action.

Explainability

Key symptoms driving the decision are listed alongside it.

Safety net

Users are told to seek help sooner if things worsen.

⚙ Under the hood

A chatbot symptom checker that provides recommendations for home care versus seeking medical attention based on the patient's reported symptoms.

CanvasBiomedicine

2D · HTML5 Canvas 2D · 60 FPS target · runs fully client-side, no install

What did you find?

Add reproduction steps (optional)