Model Card
The model is part of the Nivra AI Healthcare Assistant project, designed to help Indian patients understand and classify their symptoms accurately.
Model Details
Model Description
This is a fine-tuned version of
ClinicalBERT specifically trained for symptom classification in the Indian healthcare context. The model is part of the
Nivra AI Healthcare Assistant project, designed to help Indian patients understand and classify their symptoms accurately.
Developed by: datdevsteve
Model Type: Text Classification
Language: English (Medical Terminology)
Base Model: medicalai/ClinicalBERT
License: MIT
Model Sources [optional]
- Repository: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
- Symptom Classification: Classify patient symptom descriptions into medical condition categories
- Healthcare Triage: Assist in initial assessment of symptom severity
- Medical Chatbots: Power conversational AI for healthcare assistance
- Health Screening Apps: Automated preliminary health assessments
Out-of-Scope Use
- ❌ Not for medical diagnosis: This model provides guidance, not diagnosis
- ❌ Not a replacement for doctors: Always consult healthcare professionals
- ❌ Not for emergency triage: Use proper emergency services for critical cases
- ❌ Not for prescription: Cannot recommend medications or treatments
Limitations and Bias
Limitations
- Language: Trained on English text only; may not perform well on other Indian languages
- Context: Optimized for common conditions; may underperform on rare diseases
- Cultural Context: While trained on Indian data, may not capture all regional variations
- Symptom Complexity: Works best with clear symptom descriptions; ambiguous cases may have lower accuracy
- Comorbidities: May not fully capture complex cases with multiple concurrent conditions
Known Biases
- Geographic Bias: Training data primarily from urban Indian healthcare settings
- Age Bias: Better performance on adult symptoms (20-60 years) due to data distribution
- Gender: Balanced training data, but some gender-specific conditions may have lower support
Socioeconomic: Terminology reflects middle-class Indian healthcare context
How to Get Started with the Model
Using Transformers Library
1from transformers import AutoTokenizer, AutoModelForSequenceClassification
2import torch
3
4# Load model and tokenizer
5model_name = "your-username/clinicalbert-indian-symptoms"
6tokenizer = AutoTokenizer.from_pretrained(model_name)
7model = AutoModelForSequenceClassification.from_pretrained(model_name)
8
9# Prepare input
10text = "I have fever, headache and body pain for 2 days"
11inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
12
13# Get prediction
14with torch.no_grad():
15 outputs = model(**inputs)
16 logits = outputs.logits
17 probs = torch.softmax(logits, dim=-1)
18 predicted_class = torch.argmax(probs, dim=-1).item()
19
20# Get label
21label = model.config.id2label[predicted_class]
22confidence = probs[predicted_class].item()
23
24print(f"Condition: {label}")
25print(f"Confidence: {confidence:.2%}")
Using Pipeline
1from transformers import pipeline
2
3classifier = pipeline(
4 "text-classification",
5 model="your-username/clinicalbert-indian-symptoms",
6 top_k=5
7)
8
9result = classifier("I have persistent cough and chest congestion")
10print(result)
Training Details
Training Data
The Training Data is based off a compilation of 3 Kaggle Datasets:
Training Procedure
Preprocessing:
- Text normalization and cleaning
- Medical term standardization
- Tokenization using ClinicalBERT tokenizer
- Max sequence length: 512 tokens
Training Hyperparameters:
1{
2 "learning_rate": 2e-5,
3 "batch_size": 16,
4 "num_epochs": 5,
5 "warmup_steps": 500,
6 "weight_decay": 0.01,
7 "optimizer": "AdamW",
8 "lr_scheduler": "linear",
9 "max_seq_length": 512
10}
Hardware:
Training Time: ~3 hours
GPU: NVIDIA T4 (16GB)
Framework: PyTorch 2.1.0, Transformers 4.36.0
Evaluation
Testing Data, Factors & Metrics
Testing Data
Using test dataset made in compiled dataset splits
Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
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Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
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Software
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