HemoGuard AI

In India, over 100,000 children live with Thalassemia, a condition that requires lifelong blood transfusions. The families behind these children face overwhelming challenges: lack of access, donor unavailability, last-minute coordination, and life-threatening delays.

Our team was inspired by a story shared by Blood Warriors, where a child missed a crucial transfusion due to mismatched availability and hospital logistics. We realized AI could be the missing link—not to replace doctors or donors—but to connect them faster, smarter, and with precision.

We asked: What if technology could predict blood demand before it becomes urgent? What if it could recommend the right donor, the right time, the right blood bag?

That’s how HemoGuard AI was born.

What We Learned Real-world healthcare problems often stem from gaps in coordination, not in resources.

Predictive AI can be applied meaningfully even in low-resource environments.

Impactful solutions come from empathy, not just code.

How We Built It (Conceptually) Though this hackathon is idea-focused, we’ve envisioned a scalable solution using a modular design, combining AI prediction, donor matching, and logistics alerts:

Data Sources: Donor profiles, hospital blood bank inventory, Thalassemia patient transfusion history.

AI Prediction Engine:

Predicts next expected transfusion date.

Forecasts blood demand by region.

Uses ML models (e.g., LSTM, Random Forest).

Smart Matching: Finds geographically closest compatible donors, filters by recency of donation, health history, etc.

Emergency Alerts: In case of mismatch or shortage, sends real-time SMS/WhatsApp alerts to standby donors.

Guardian Dashboard: For parents/caretakers to view upcoming transfusions, donor status, and risk alerts.

Challenges We Anticipate Data Privacy: Health data must be securely encrypted and anonymized.

Donor Fatigue: Building a sustainable network of recurring donors.

Integration with Hospitals: Convincing hospitals to adopt the dashboard will require fieldwork and partnerships.

Funding and Outreach: Scaling this from prototype to real-world use requires NGO and government support.

Built With

• bayes
• built-with-(envisioned-stack)-languages:-python
• canva-for-visual-assets-ai-models:-time-series-(lstm)
• decision-trees
• donor
• for
• google-maps-api-for-nearest-donor-logistics-platform:-android-(for-donor-app)
• javascript-(react-for-frontend)-frameworks:-flask-/-fastapi-(backend)
• naive
• tensorflow-(for-ai)-database:-firebase-firestore-or-mongodb-atlas-apis:-twilio/whatsapp-for-donor-alerts
• web-(for-hospital/caretaker-dashboard)-design-tools:-figma-for-ui-prototyping