Introduction
Analysis of unstructured clinical notes is a critical task in healthcare. DSPy can automate the extraction of symptoms, medications, and diagnoses, as well as flag potential risks.
System Architecture
The system breaks down clinical note analysis into modular tasks: Entity Extraction, Risk Analysis, Summarization, and Recommendations.
Medical Entity Extraction
Using ChainOfThought to identify and structure entities creates a robust foundation for downstream tasks.
class MedicalEntityExtractor(dspy.Module):
def __init__(self):
self.extract = dspy.ChainOfThought(
"clinical_note -> medical_entities, patient_symptoms, diagnoses, medications, vitals"
)
def forward(self, clinical_note):
with dspy.context(medical_context=True):
return self.extract(clinical_note=clinical_note)Clinical Risk Analysis
Once entities are extracted, a separate module assesses risks and checks for drug interactions.
class ClinicalRiskAnalyzer(dspy.Module):
def __init__(self):
self.analyze_risk = dspy.Predict(
"entities, symptoms, meds -> risk_factors, alert_level"
)Optimization with MIPRO
Medical vernacular is complex. Optimizing prompts with MIPRO using a labeled dataset of clinical notes significantly improves F1 scores on entity extraction.