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Medical Templates

Medical text analysis and extraction.

Overview

Medical templates are designed for extracting clinical information from medical texts.

Disclaimer: These templates are for research and analysis purposes only. Not for clinical decision-making without professional review.

Templates

anatomy_graph

Type: graph

Purpose: Extract anatomical structures and relationships

Best for: - Anatomy textbooks - Surgical reports - Medical education materials

Entities:

  • Body parts
  • Organs
  • Systems
  • Structures

Relations:

  • part_of — Anatomical hierarchy
  • connected_to — Structural connections
  • supplies — Blood/nerve supply
he parse anatomy.md -t medicine/anatomy_graph -l en

ka = Template.create("medicine/anatomy_graph", "en")
result = ka.parse(anatomy_text)

print(f"Structures: {len(result.nodes)}")
print(f"Relationships: {len(result.edges)}")

# Build index for interactive exploration
result.build_index()
result.show()

discharge_instruction

Type: model

Purpose: Extract discharge summary information

Best for: - Discharge summaries - Transfer notes - After-visit summaries

Fields:

Field Type Description
diagnosis str Primary diagnosis
procedures list[str] Procedures performed
medications list[str] Prescribed medications
follow_up str Follow-up instructions
warnings list[str] Warning signs 
he parse discharge.md -t medicine/discharge_instruction -l en

ka = Template.create("medicine/discharge_instruction", "en")
summary = ka.parse(discharge_text)

print(f"Diagnosis: {summary.data.diagnosis}")
print(f"Follow-up: {summary.data.follow_up}")

drug_interaction

Type: graph

Purpose: Extract drug interactions and relationships

Best for: - Drug databases - Pharmacy references - Medication reconciliation

Entities:

  • Drugs
  • Drug classes
  • Effects

Relations:

  • interacts_with — Drug interactions
  • contraindicated_with — Contraindications
  • synergizes_with — Synergistic effects
he parse drug_ref.md -t medicine/drug_interaction -l en

ka = Template.create("medicine/drug_interaction", "en")
result = ka.parse(drug_text)

print(f"Drugs: {len(result.nodes)}")
for relation in result.edges:
    print(f"{relation.source} {relation.type} {relation.target}")

hospital_timeline

Type: temporal_graph

Purpose: Extract hospital stay timeline

Best for: - Hospital course notes - Progress notes - Transfer summaries

Features:

  • Admission/discharge dates
  • Procedures and events
  • Timeline visualization
he parse hospital_notes.md -t medicine/hospital_timeline -l en

ka = Template.create("medicine/hospital_timeline", "en")
result = ka.parse(notes_text)

print(f"Events: {len(result.nodes)}")
for edge in result.edges:
    if hasattr(edge, 'time'):
        print(f"  {edge.time}: {edge.source} -> {edge.target}")

# Visualize timeline
result.build_index()
result.show()

treatment_map

Type: graph

Purpose: Extract treatment protocols and pathways

Best for: - Treatment guidelines - Care pathways - Protocol documents

he parse protocol.md -t medicine/treatment_map -l en

ka = Template.create("medicine/treatment_map", "en")
result = ka.parse(protocol_text)

print(f"Steps: {len(result.nodes)}")
print(f"Flow relationships: {len(result.edges)}")

# Visualize treatment pathway
result.build_index()
result.show()

Use Cases

Discharge Summary Analysis

from hyperextract import Template

ka = Template.create("medicine/discharge_instruction", "en")
discharges = []

for file in discharge_files:
    summary = ka.parse(file.read_text())
    discharges.append(summary.data)

# Analyze common diagnoses
from collections import Counter
diagnoses = Counter(d.diagnosis for d in discharges)
print(diagnoses.most_common(10))

Drug Interaction Network

ka = Template.create("medicine/drug_interaction", "en")
network = ka.parse(drug_database)

# Find interactions for specific drug
drug = "Warfarin"
interactions = [
    r for r in network.data.relations
    if r.source == drug or r.target == drug
]

for i in interactions:
    print(f"{i.source} {i.type} {i.target}")

Anatomy Education

ka = Template.create("medicine/anatomy_graph", "en")
anatomy = ka.parse(textbook_chapter)

# Build index for interactive visualization
anatomy.build_index()

# Visualize with search/chat capabilities
anatomy.show()

# Search
results = anatomy.search("nerves in hand")

Tips

  1. discharge_instruction for summaries — Quick extraction of key discharge info
  2. drug_interaction for safety — Build interaction checking tools
  3. anatomy_graph for education — Create interactive anatomy maps
  4. hospital_timeline for courses — Track patient journeys

Data Privacy

When working with medical data: - Ensure HIPAA compliance - De-identify data before processing - Use secure environments - Follow institutional policies

See Also