A prototype built for the Pristina Healthcare Hackathon 2025, addressing the challenge 🎯 “Digital Health Literacy: Empowering Smart Choices” proposed by Technical University of Munich.
🔗 Challenge Description
In a world flooded with health advice online, people like Nora — a busy parent looking for help — often end up confused or misinformed. Sano aims to solve this by providing a simple and explainable tool that checks whether health claims are supported by evidence-based sources like Mayo Clinic, PubMed, and WHO.
By analyzing health claims, highlighting suspicious keywords, and comparing them to a trusted corpus, Sano NLP delivers a verdict:
✅ Trusted | ⚠ Unclear | ❌ Risky — to help users make smart, informed decisions.
- Loads 1,000+ Mayo Clinic health articles (
mayo_corpus.json) - Cleans raw HTML and extracts readable medical content using
BeautifulSoup
- Uses
pritamdeka/BioBERTviasentence-transformers - Converts both the health claims and corpus into 768-dimensional vectors for comparison
- Vector search engine (
QdrantClient) indexes and retrieves top-k most relevant articles based on cosine similarity - Fast, memory-efficient, and supports payload filtering
- Preprocesses user claim using
spaCy: tokenization, stopword removal, lemmatization - Flags suspicious health keywords (e.g., detox, miracle, guaranteed) that often appear in misinformation
- Identifies named entities (e.g., DISEASE, DRUG, TREATMENT)
- Scores from semantic similarity are interpreted into:
- Trusted (score ≥ 0.7)
- Unclear (0.5 ≤ score < 0.7)
- Risky (score < 0.5)
Claim Input:
“Simply exposing your spine to sunlight for 15 minutes resets your DNA and eliminates chronic diseases...”
System Output:
- ✅ Claim analyzed and embedded
- ⚠ Detected suspicious word:
detox - 🔍 Top 5 matches retrieved from Mayo Clinic
- 🧠 Verdict: Unclear
- Python 3.8+
- sentence-transformers – BioBERT embeddings for semantic similarity
- Qdrant – Vector search engine for document retrieval
- spaCy – NLP preprocessing (tokenization, NER, lemmatization)
- BeautifulSoup – HTML cleaning
- Mayo Clinic Corpus – Trusted medical content for baseline verification
| File | Description |
|---|---|
Sano_NLP.ipynb |
Main notebook containing the full prototype pipeline |
mayo_corpus.json |
Cleaned JSON corpus of Mayo Clinic articles (required for local inference) |
- 🌐 Add multilingual support (e.g., German, Albanian)
- 📊 Add explainable scores for trust and risk
- 🤖 Integrate with Chat interfaces or WhatsApp bot
- 🔬 Expand corpus with PubMed abstracts, WHO guidelines
- 📱 Turn into a mobile or web app (e.g., via Streamlit or Flask)
Challenge Title: Digital Health Literacy: Empowering Smart Choices
Provided by: Technical University of Munich (TUM)
Challenge Description:
Design an engaging and accessible solution that helps individuals improve their digital health literacy.
Equip people like Nora — a working mother overwhelmed by online misinformation — with a tool they can trust.
Full description: https://pristina.innovate.healthcare/challenges/
Built by Ayusee Swain at the Pristina Healthcare Hackathon 2025.
Thanks to the mentors, healthcare experts, and challenge partners from TUM for their guidance and support.