Welcome to the BioMed Healthcare Chatbot project - an intelligent medical assistant designed to help understand and analyze patient symptoms through conversational AI.

This chatbot bridges the gap between patients and preliminary medical screening by providing an accessible, interactive interface for symptom assessment. Using advanced natural language processing and machine learning, the system offers evidence-based insights to support healthcare decision-making.

The BioMed Healthcare Chatbot is an enterprise-grade diagnostic assistant that interprets patient symptoms and provides preliminary medical insights. The system uses advanced natural language processing to understand medical queries and deliver evidence-based diagnostic suggestions.

• Advanced NLP Processing: Utilizes BioBERT transformer model fine-tuned on biomedical corpora
• Interactive Diagnosis: Real-time symptom processing with intelligent follow-up questions
• Medical Entity Recognition: Extracts and analyzes clinical entities from patient input
• Comprehensive Training: Fine-tuned on 50,000+ clinical records for robust performance

• Python 3.7 or higher
• pip3 package manager
• Virtual environment (recommended)

1. Clone the repository

   git clone <repository-url>
   cd healthcare-chatbot

2. Create virtual environment (recommended)

   python3 -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

   pip3 install -r requirements.txt

4. Verify data structure

   Ensure the following directories exist with proper data files:

   healthcare-chatbot/
   ├── Master Data/
   │   ├── training_data.csv
   │   └── test_data.csv
   ├── Data/
   │   └── processed_data/
   └── chat_bot.py
   

cd healthcare-chatbot
python3 chat_bot.py

========================================HealthCare ChatBot========================================

Your Name? -->John

Enter the symptom you are experiencing -->fever

Okay, From how many days? : 3
Are you experiencing any back_pain? : no
weakness_in_limbs? : no
neck_pain? : yes
dizziness? : no

[Diagnostic output with confidence scores and recommendations]

• Base Model: BioBERT (Biomedical BERT)
• Framework: PyTorch
• NLP Pipeline: Custom tokenization and entity extraction
• Training Method: Transfer learning with domain-specific fine-tuning

1. Clinical record preprocessing and normalization
2. Medical entity extraction using BioBERT embeddings
3. Symptom-disease mapping with confidence scoring
4. Multi-label classification for complex cases

The model training approach includes:

• Dataset: Comprehensive collection of annotated clinical records
• Training Strategy: Transfer learning with BioBERT base model
• Validation: Rigorous cross-validation on medical entity extraction
• Optimization: Adam optimizer with learning rate scheduling
• Evaluation: Multi-metric assessment including precision and recall

Project Duration: January 2024 – May 2024

This project demonstrates expertise in:

• Clinical NLP with structured model evaluation frameworks
• Systematic testing and validation techniques for medical AI systems
• Fine-tuning methodologies for domain-specific transformer models
• Practical application of predictive modeling in healthcare diagnostics

• Multi-language support for broader accessibility
• Integration with electronic health records (EHR)
• Expanded disease taxonomy coverage
• Real-time learning from clinical feedback
• API development for healthcare system integration

This chatbot is designed for research and preliminary screening purposes only. It is not a substitute for professional medical advice, diagnosis, or treatment. Always consult qualified healthcare providers for medical concerns.

This is an internal organizational project. For contribution guidelines and access permissions, please contact the project maintainers.

Internal organizational use only. All rights reserved.

For questions, issues, or collaboration opportunities, please reach out to the development team.

• Shamya Haria • Arya Agrawal

Note: This repository contains proprietary medical NLP models and datasets. Ensure compliance with HIPAA and relevant data protection regulations when handling patient information.