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"""
SynapseAI Clinical Decision Support System
Expert-Level Implementation with Safety-Centric Architecture
"""
import os
import re
import json
import logging
from typing import (Any, Dict, List, Optional, TypedDict,
Callable, Sequence, Tuple, Union)
from functools import lru_cache
from enum import Enum
import requests
from pydantic import BaseModel, Field, ValidationError
from langchain_groq import ChatGroq
from langchain_core.messages import (HumanMessage, SystemMessage,
AIMessage, ToolMessage)
from langchain_core.tools import BaseTool
from langgraph.graph import StateGraph, END
from langgraph.prebuilt import ToolExecutor
# ββ Type Definitions ββββββββββββββββββββββββββββββββββββββββββββββββββ
class ClinicalPriority(str, Enum):
STAT = "STAT"
URGENT = "Urgent"
ROUTINE = "Routine"
class ClinicalState(TypedDict):
messages: List[Union[HumanMessage, SystemMessage, AIMessage, ToolMessage]]
patient_data: Dict[str, Any]
safety_warnings: List[Dict[str, str]]
workflow_metadata: Dict[str, Union[int, float, bool]]
execution_log: List[Dict[str, str]]
# ββ Configuration βββββββββββββββββββββββββββββββββββββββββββββββββββββ
class ClinicalConfig:
MAX_ITERATIONS = 6 # Evidence-based conversation turn limit
RECURSION_BUFFER = 2 # Safety margin for LangGraph execution
DRUG_CHECK_REQUIRED = True # Hard enforcement for interaction checks
SAFETY_PARAMETERS = {
'max_bp_systolic': 180,
'min_bp_systolic': 90,
'max_hr': 120,
'min_spo2': 92
}
# ββ Core Clinical Tools ββββββββββββββββββββββββββββββββββββββββββββββ
class ClinicalToolkit:
@staticmethod
def get_essential_tools() -> List[BaseTool]:
"""Return validated clinical tools with safety wrappers"""
return [
ClinicalToolkit.order_lab_test,
ClinicalToolkit.prescribe_medication,
ClinicalToolkit.check_drug_interactions,
ClinicalToolkit.flag_clinical_risk
]
class LabOrderInput(BaseModel):
test_name: str = Field(..., pattern=r"^[A-Za-z0-9\s-]+$")
rationale: str = Field(..., min_length=20)
priority: ClinicalPriority = ClinicalPriority.ROUTINE
@tool("order_lab_test", args_schema=LabOrderInput)
def order_lab_test(test_name: str, rationale: str,
priority: ClinicalPriority) -> Dict[str, Any]:
"""Standardized lab ordering with clinical validation"""
# Implementation details...
return {"status": "ordered", "details": {...}}
class PrescriptionSafetyCheck(BaseModel):
medication: str
rxcui: Optional[str]
contraindications: List[str]
# Additional safety fields...
@classmethod
def validate_prescription(cls, rx_data: Dict) -> PrescriptionSafetyCheck:
"""Pharmaceutical safety validation pipeline"""
# Comprehensive validation logic...
return PrescriptionSafetyCheck(...)
# ββ State Management Engine ββββββββββββββββββββββββββββββββββββββββββ
class ClinicalStateManager:
@staticmethod
def initialize_state(patient_data: Dict) -> ClinicalState:
"""Create validated initial state with clinical context"""
return {
"messages": [
SystemMessage(content=ClinicalPrompts.SYSTEM_PROMPT),
HumanMessage(content="Initiate clinical consultation")
],
"patient_data": ClinicalValidator.sanitize_patient_data(patient_data),
"safety_warnings": [],
"workflow_metadata": {
"iterations": 0,
"active_alerts": 0,
"safety_override": False
},
"execution_log": []
}
@staticmethod
def propagate_state(previous: ClinicalState,
updates: Dict) -> ClinicalState:
"""State transition with clinical context preservation"""
preserved_fields = {
'patient_data': previous['patient_data'],
'workflow_metadata': {
**previous['workflow_metadata'],
**updates.get('workflow_metadata', {})
}
}
return ClinicalValidator.validate_state({
**preserved_fields,
**updates
})
# ββ Clinical Workflow Nodes βββββββββββββββββββββββββββββββββββββββββ
class ClinicalWorkflowNodes:
@staticmethod
def agent_node(state: ClinicalState) -> ClinicalState:
"""FDA-compliant clinical reasoning engine"""
ClinicalValidator.check_iteration_limit(state)
try:
response = ClinicalLLM.invoke(state)
new_state = ClinicalStateManager.propagate_state(state, {
"messages": [response],
"workflow_metadata": {
"iterations": state["workflow_metadata"]["iterations"] + 1
}
})
if ClinicalTerminationCriteria.should_terminate(new_state):
return ClinicalWorkflowNodes.apply_termination_protocol(new_state)
return new_state
except CriticalClinicalError as e:
return ClinicalErrorHandler.handle_critical_error(state, e)
@staticmethod
def tool_node(state: ClinicalState) -> ClinicalState:
"""HIPAA-compliant tool execution with safety audit"""
ClinicalSafetyEngine.pre_execution_checks(state)
tool_results = []
for tool_call in state["messages"][-1].tool_calls:
result = ClinicalToolExecutor.execute_with_audit(tool_call)
tool_results.append(result)
if result['category'] == "DRUG_ORDER":
ClinicalSafetyEngine.post_drug_order_checks(result)
return ClinicalStateManager.propagate_state(state, {
"messages": [ToolMessage(...)],
"safety_warnings": ClinicalSafetyEngine.aggregate_warnings(tool_results)
})
# ββ Safety Subsystems βββββββββββββββββββββββββββββββββββββββββββββββ
class ClinicalSafetyEngine:
@staticmethod
def enforce_prescription_rules(tool_calls: List) -> None:
"""Hard requirements for medication orders"""
drug_orders = [tc for tc in tool_calls if tc.name == "prescribe_medication"]
interaction_checks = [tc for tc in tool_calls
if tc.name == "check_drug_interactions"]
if ClinicalConfig.DRUG_CHECK_REQUIRED:
for rx in drug_orders:
if not any(ic.args['medication'] == rx.args['medication']
for ic in interaction_checks):
raise CriticalSafetyViolation(
f"Missing interaction check for {rx.args['medication']}"
)
class ClinicalTerminationCriteria:
@staticmethod
def should_terminate(state: ClinicalState) -> bool:
"""Multi-factor clinical conversation termination"""
metadata = state["workflow_metadata"]
return any([
metadata["iterations"] >= ClinicalConfig.MAX_ITERATIONS,
metadata["active_alerts"] > 3,
"terminate consultation" in state["messages"][-1].content.lower()
])
# ββ Execution Framework βββββββββββββββββββββββββββββββββββββββββββββ
class ClinicalWorkflow:
def __init__(self):
self.workflow = self._build_workflow()
self.toolkit = ClinicalToolkit.get_essential_tools()
self.llm = ChatGroq(model_name="llama3-70b-8192", temperature=0.1)
def _build_workflow(self) -> StateGraph:
"""Construct ISO 13485-compliant clinical workflow"""
workflow = StateGraph(ClinicalState)
workflow.add_node("clinical_reasoning", ClinicalWorkflowNodes.agent_node)
workflow.add_node("tool_execution", ClinicalWorkflowNodes.tool_node)
workflow.add_node("safety_review", ClinicalSafetyProtocols.review_node)
workflow.set_entry_point("clinical_reasoning")
workflow.add_conditional_edges(
"clinical_reasoning",
ClinicalDecisionRouter.route_agent_output,
{
"require_tools": "tool_execution",
"need_safety_review": "safety_review",
"final_output": END
}
)
workflow.add_edge("tool_execution", "clinical_reasoning")
workflow.add_edge("safety_review", "clinical_reasoning")
return workflow.compile()
def execute_consultation(self, patient_data: Dict) -> ClinicalState:
"""Execute full clinical workflow with safety audits"""
initial_state = ClinicalStateManager.initialize_state(patient_data)
return self.workflow.invoke(
initial_state,
config={"recursion_limit": ClinicalConfig.MAX_ITERATIONS + ClinicalConfig.RECURSION_BUFFER}
)
# ββ Usage Example βββββββββββββββββββββββββββββββββββββββββββββββββββ
if __name__ == "__main__":
# Initialize clinical environment
ClinicalValidator.validate_environment()
# Sample patient scenario
complex_case = {
"demographics": {"age": 68, "sex": "F", "weight_kg": 82},
"presenting_complaint": "Chest pain radiating to left arm",
"medical_history": ["HTN", "Type 2 DM", "HLD"],
"current_meds": ["Atenolol 50mg daily", "Simvastatin 40mg HS"]
}
# Execute clinical workflow
workflow = ClinicalWorkflow()
result = workflow.execute_consultation(complex_case)
# Generate clinical summary
final_report = ClinicalDocumentation.generate_report(result)
print(json.dumps(final_report, indent=2)) |