DSN Bootcamp 2024: Leveraging Graph-based Techniques for Enhanced Learning in Language Models by Emeka Okoye

Implementation Guide: Medical QA System with Graph Integration

1. Environment Setup and Dependencies

# Core dependencies
pip install torch transformers torch_geometric networkx pandas
pip install spacy scikit-learn
python -m spacy download en_core_web_sm

# Optional visualization
pip install matplotlib seaborn

2. Knowledge Graph Implementation

2.1 Define Graph Schema

class MedicalEntity:
    def __init__(self, id, name, type):
        self.id = id
        self.name = name
        self.type = type  # medication, condition, side_effect
        self.attributes = {}

class Relationship:
    def __init__(self, source, target, type, weight=1.0):
        self.source = source
        self.target = target
        self.type = type  # treats, causes, contraindicates
        self.weight = weight

2.2 Graph Construction

class MedicalKnowledgeGraph:
    def __init__(self):
        self.graph = nx.Graph()
        self.entity_embeddings = {}
        
    def add_entity(self, entity):
        self.graph.add_node(entity.id, 
                           name=entity.name, 
                           type=entity.type, 
                           attributes=entity.attributes)
        
    def add_relationship(self, relationship):
        self.graph.add_edge(relationship.source, 
                           relationship.target,
                           type=relationship.type, 
                           weight=relationship.weight)

3. Neural Network Components

3.1 Graph Neural Network

class MedicalGNN(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super().__init__()
        self.conv1 = GCNConv(input_dim, hidden_dim)
        self.conv2 = GCNConv(hidden_dim, output_dim)
        self.dropout = nn.Dropout(0.2)
        
    def forward(self, x, edge_index, edge_attr=None):
        x = self.conv1(x, edge_index)
        x = F.relu(x)
        x = self.dropout(x)
        x = self.conv2(x, edge_index)
        return x

3.2 Integration Layer

class IntegrationLayer(nn.Module):
    def __init__(self, llm_dim, gnn_dim, output_dim):
        super().__init__()
        self.attention = nn.MultiheadAttention(llm_dim, num_heads=8)
        self.fusion = nn.Linear(llm_dim + gnn_dim, output_dim)
        
    def forward(self, llm_features, graph_features):
        attended_features, _ = self.attention(llm_features, 
                                           graph_features, 
                                           graph_features)
        combined = torch.cat([attended_features, graph_features], dim=-1)
        return self.fusion(combined)

4. Query Processing Pipeline

4.1 Entity Extraction

class EntityExtractor:
    def __init__(self):
        self.nlp = spacy.load('en_core_web_sm')
        
    def extract_entities(self, text):
        doc = self.nlp(text)
        medical_entities = []
        for ent in doc.ents:
            if ent.label_ in ['DRUG', 'CONDITION', 'SYMPTOM']:
                medical_entities.append({
                    'text': ent.text,
                    'label': ent.label_,
                    'start': ent.start_char,
                    'end': ent.end_char
                })
        return medical_entities

4.2 Graph Query Engine

class GraphQueryEngine:
    def __init__(self, knowledge_graph):
        self.kg = knowledge_graph
        self.cache = {}
        
    def query(self, entities, relation_types=None):
        cache_key = f"{'-'.join(entities)}_{'-'.join(relation_types or [])}"
        if cache_key in self.cache:
            return self.cache[cache_key]
            
        subgraph = self.kg.get_subgraph(entities, relation_types)
        self.cache[cache_key] = subgraph
        return subgraph

5. Response Generation

5.1 Response Template Manager

class ResponseTemplate:
    def __init__(self):
        self.templates = {
            'side_effect': "Based on medical knowledge, {medication} may cause: {effects}",
            'contraindication': "CAUTION: {condition} may be a contraindication for {medication}",
            'interaction': "Potential interaction between {med1} and {med2}: {details}"
        }
    
    def format_response(self, template_key, **kwargs):
        template = self.templates[template_key]
        return template.format(**kwargs)

5.2 Safety Checker

class SafetyChecker:
    def __init__(self):
        self.disclaimers = [
            "Consult healthcare provider for medical advice.",
            "This information is for educational purposes only."
        ]
    
    def check_response(self, response):
        # Add safety checks and disclaimers
        checked_response = response + "\n\n"
        checked_response += "IMPORTANT: " + " ".join(self.disclaimers)
        return checked_response

6. Main System Integration

class MedicalQASystem:
    def __init__(self):
        self.kg = MedicalKnowledgeGraph()
        self.gnn = MedicalGNN(input_dim=768, hidden_dim=256, output_dim=128)
        self.llm = AutoModel.from_pretrained('bert-base-uncased')
        self.entity_extractor = EntityExtractor()
        self.query_engine = GraphQueryEngine(self.kg)
        self.integration_layer = IntegrationLayer(768, 128, 256)
        self.response_generator = ResponseTemplate()
        self.safety_checker = SafetyChecker()
        
    def process_query(self, question):
        # 1. Extract entities
        entities = self.entity_extractor.extract_entities(question)
        
        # 2. Get LLM features
        llm_features = self.llm(question)
        
        # 3. Query knowledge graph
        graph_data = self.query_engine.query(entities)
        
        # 4. Process with GNN
        graph_features = self.gnn(graph_data)
        
        # 5. Integrate information
        combined_features = self.integration_layer(llm_features, graph_features)
        
        # 6. Generate response
        response = self.response_generator.format_response(
            template_key='side_effect',
            medication=entities[0]['text'],
            effects=graph_data['side_effects']
        )
        
        # 7. Safety check
        safe_response = self.safety_checker.check_response(response)
        
        return safe_response

7. Deployment and Monitoring

7.1 Performance Metrics

class PerformanceMonitor:
    def __init__(self):
        self.metrics = {
            'response_time': [],
            'accuracy': [],
            'entity_extraction_success': [],
            'graph_query_hits': []
        }
    
    def log_metric(self, metric_name, value):
        self.metrics[metric_name].append(value)
        
    def get_summary(self):
        return {k: np.mean(v) for k, v in self.metrics.items()}

7.2 Cache Management

class CacheManager:
    def __init__(self, max_size=1000):
        self.max_size = max_size
        self.cache = OrderedDict()
    
    def get(self, key):
        if key in self.cache:
            self.cache.move_to_end(key)
            return self.cache[key]
        return None
    
    def put(self, key, value):
        if len(self.cache) >= self.max_size:
            self.cache.popitem(last=False)
        self.cache[key] = value
        self.cache.move_to_end(key)

8. Usage Example

# Initialize the system
qa_system = MedicalQASystem()

# Process a question
question = "What are the potential side effects of metformin for a person with kidney issues?"
response = qa_system.process_query(question)

print(response)