emergency_blood_request_classifier

🩸 Emergency Blood Request Classifier

A fine-tuned DistilBERT model for binary text classification that automatically determines whether a blood donation request is an emergency or not an emergency.

This model is part of my personal project to build an AI-powered blood donation mobile application for Sri Lanka — designed to prioritize life-critical requests and alert nearby donors faster.

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📌 Model Summary

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🎯 Intended Use

Primary Use

This model is designed to be integrated into a Sri Lanka blood donation mobile application. When a user posts a blood request, the model reads the description and classifies it as:

• 🚨 emergency → triggers immediate alerts to nearby donors
• 📋 not_emergency → listed normally in the donor feed

How It Fits the Bigger Picture

Blood Donation App (Sri Lanka)
        │
        ▼
  User posts a blood request
        │
        ▼
  🤖 This Model (Emergency Classifier)
  ─────────────────────────────────────
  Reads the request description
        │
        ├──▶ emergency     → 🚨 Immediately alert nearby donors
        │
        └──▶ not_emergency → 📋 List normally in the donor feed

Other Potential Uses

• Urgency triage in healthcare communication platforms
• Benchmarking lightweight NLP models on medical emergency detection
• Research on urgency language patterns in South Asian healthcare contexts

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⚠️ Limitations

• Synthetic training data — The model was trained on AI-generated data. Real-world requests may use different phrasing, slang, abbreviations, or informal language not well-represented in training.
• English only — Sri Lankan blood requests often appear in Sinhala or Tamil. This model does not support those languages.
• Sri Lanka context — The dataset references Sri Lankan hospitals and cities. Performance on requests from other regions may vary.
• Not for clinical use — This model must not be used as a substitute for medical triage or clinical decision-making. It is an assistive tool for a donor coordination platform only.

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🚀 Quick Start

Using the Pipeline (Recommended)

from transformers import pipeline

classifier = pipeline("text-classification", model="AshenFdo/emergency_blood_request_classifier")

result = classifier("O negative blood required urgently at Karapitiya Hospital. We are out of time.")
print(result)
# [{'label': 'emergency', 'score': 0.999...}]

result = classifier("Organizing AB+ blood for an upcoming planned operation at Kandy National Hospital.")
print(result)
# [{'label': 'not_emergency', 'score': 0.999...}]

Loading Model Directly

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("AshenFdo/emergency_blood_request_classifier")
model = AutoModelForSequenceClassification.from_pretrained("AshenFdo/emergency_blood_request_classifier")

text = "EMERGENCY: Kandy National Hospital ICU urgently needs A- blood. Patient is critical."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)

with torch.no_grad():
    logits = model(**inputs).logits

predicted_class_id = logits.argmax().item()
label = model.config.id2label[predicted_class_id]
print(f"Prediction: {label}")
# Prediction: emergency

Batch Inference

from transformers import pipeline

classifier = pipeline("text-classification", model="AshenFdo/emergency_blood_request_classifier")

requests = [
    "Need O- blood for a routine hospital visit at Negombo Hospital.",
    "My sister is in Kandy Hospital ICU with organ failure. We desperately need B+ blood. Pls help!",
    "Community blood donation drive at Colombo town hall this Saturday.",
    "URGENT: Karapitiya hospital needs AB- blood. Bus accident, multiple casualties.",
]

results = classifier(requests)
for req, res in zip(requests, results):
    print(f"[{res['label']}] {req[:60]}...")

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📊 Training Details

Dataset

• 2,500 synthetic blood request descriptions
• Balanced — 1,250 emergency and 1,250 not_emergency
• Sri Lanka-specific hospital names and geographic references
• See the full dataset: AshenFdo/synthetic_blood_request_urgency_dataset

Label Definitions

Hyperparameters

Training Results

Framework Versions

• Transformers: 5.10.1
• PyTorch: 2.11.0+cu128
• Datasets: 5.0.0
• Tokenizers: 0.22.2
• Training Environment: Google Colab (GPU)

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🗂️ Example Predictions

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🔗 Related Resources

• 📁 Full Project on GitHub: AshenFdo/Blood-Request-Emergency-Classification-Model
• 🗃️ Training Dataset: AshenFdo/synthetic_blood_request_urgency_dataset
• 🤗 Base Model: distilbert/distilbert-base-uncased

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📜 Citation

If you use this model in your research or project, please cite it as:

@model{AshenFdo_emergency_blood_request_classifier_2025,
  author    = {AshenFdo},
  title     = {Emergency Blood Request Classifier},
  year      = {2025},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/AshenFdo/emergency_blood_request_classifier}
}

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Built with the goal of saving lives — one donation at a time. 🩸