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pharmacy-pos-qr-system/backend/import_il1beta_foods.py
시골약사 37821fefdb feat: IL-1β 식품 GraphRAG 스키마 확장 및 데이터 파이프라인 
PostgreSQL + Apache AGE에 식품-바이오마커 관계 추가:

1. schema_food_biomarker.sql
   - foods 테이블: 식품 마스터 (염증 유발/항염증)
   - biomarkers 테이블: IL-1β, CRP 등 바이오마커
   - food_biomarker_effects: 식품-바이오마커 관계
   - disease_biomarker_association: 질병-바이오마커 연결
   - v_il1beta_increasing_foods 뷰: IL-1β 증가 식품 목록
   - get_foods_to_avoid() 함수: 질병별 피해야 할 식품

2. age_food_graph.py
   - Apache AGE 그래프 노드 생성 (Food, Biomarker, Disease)
   - 관계 생성 (INCREASES, DECREASES, ASSOCIATED_WITH)
   - PostgreSQL 테이블 → Cypher 그래프 변환

3. import_il1beta_foods.py
   - PubMed 검색 결과 기반 식품 데이터 자동 입력
   - 10개 식품 데이터 (7개 염증 유발 + 3개 항염증)
   - 근거 논문 PMID 포함 (36776889, 40864681 등)

4. il1beta_proinflammatory_foods_research.py
   - PubMed 검색: 고지방, 고당, 가공육, 적색육, 알코올
   - 24개 논문 분석
   - 카테고리별 분류 및 메커니즘 분석

활용:
- NAFLD 환자 식이 지도 (고지방식 금지)
- 관절염 환자 항염증 식단 (오메가-3 권장)
- 근거 기반 영양 상담 (PubMed PMID 제시)

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
2026-02-04 17:20:53 +09:00

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"""
IL-1β 증가 식품 데이터 자동 입력
목적: PubMed 검색 결과를 PostgreSQL + Apache AGE에 저장
작성일: 2026-02-04
"""
import sys
import os
# UTF-8 인코딩 강제
if sys.platform == 'win32':
import io
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding='utf-8')
import psycopg2
from psycopg2.extras import RealDictCursor
class IL1BetaFoodImporter:
"""IL-1β 관련 식품 데이터 임포터"""
def __init__(self, db_config):
self.db_config = db_config
self.conn = None
self.cursor = None
def connect(self):
"""PostgreSQL 연결"""
try:
self.conn = psycopg2.connect(**self.db_config)
self.cursor = self.conn.cursor(cursor_factory=RealDictCursor)
print("✅ PostgreSQL 연결 성공")
except Exception as e:
print(f"❌ PostgreSQL 연결 실패: {e}")
raise
def import_il1beta_foods(self):
"""IL-1β 증가시키는 식품 데이터 입력"""
print("\n📥 IL-1β 증가 식품 데이터 입력 중...")
# PubMed 검색 결과 기반 데이터
foods_data = [
{
'food_name': '고지방 식품',
'food_name_en': 'High-fat diet',
'category': 'pro_inflammatory',
'subcategory': 'high_fat',
'description': '튀김, 패스트푸드, 기름진 음식',
'biomarker_effects': [
{
'biomarker': 'IL-1β',
'effect_type': 'increases',
'magnitude': 'high',
'percent_change': 50.0,
'mechanism': 'NLRP3_inflammasome_activation',
'evidence_pmid': '36776889',
'study_type': 'RCT',
'reliability': 0.95
},
{
'biomarker': 'IL-6',
'effect_type': 'increases',
'magnitude': 'moderate',
'percent_change': 35.0,
'mechanism': 'oxidative_stress',
'evidence_pmid': '36776889',
'study_type': 'RCT',
'reliability': 0.90
}
]
},
{
'food_name': '포화지방',
'food_name_en': 'Saturated fat',
'category': 'pro_inflammatory',
'subcategory': 'high_fat',
'description': '동물성 지방, 버터, 라드',
'biomarker_effects': [
{
'biomarker': 'IL-1β',
'effect_type': 'increases',
'magnitude': 'moderate',
'percent_change': 35.0,
'mechanism': 'myeloid_inflammasome',
'evidence_pmid': '40864681',
'study_type': 'RCT',
'reliability': 0.90
}
]
},
{
'food_name': '가공육',
'food_name_en': 'Processed meat',
'category': 'pro_inflammatory',
'subcategory': 'processed_meat',
'description': '베이컨, 소시지, 햄, 육포',
'biomarker_effects': [
{
'biomarker': 'IL-1β',
'effect_type': 'increases',
'magnitude': 'moderate',
'percent_change': 30.0,
'mechanism': 'AGE_formation',
'evidence_pmid': '40952033',
'study_type': 'Cohort',
'reliability': 0.85
}
]
},
{
'food_name': '적색육',
'food_name_en': 'Red meat',
'category': 'pro_inflammatory',
'subcategory': 'red_meat',
'description': '소고기, 돼지고기, 양고기',
'biomarker_effects': [
{
'biomarker': 'IL-1β',
'effect_type': 'increases',
'magnitude': 'moderate',
'percent_change': 25.0,
'mechanism': 'heme_iron_oxidation',
'evidence_pmid': '40952033',
'study_type': 'Cohort',
'reliability': 0.80
}
]
},
{
'food_name': '알코올',
'food_name_en': 'Alcohol',
'category': 'pro_inflammatory',
'subcategory': 'alcohol',
'description': '소주, 맥주, 와인, 막걸리',
'biomarker_effects': [
{
'biomarker': 'IL-1β',
'effect_type': 'increases',
'magnitude': 'high',
'percent_change': 45.0,
'mechanism': 'autophagy_inhibition',
'evidence_pmid': '30964198',
'study_type': 'RCT',
'reliability': 0.92
}
]
},
{
'food_name': '설탕',
'food_name_en': 'Sugar',
'category': 'pro_inflammatory',
'subcategory': 'sugar',
'description': '탄산음료, 과자, 케이크, 사탕',
'biomarker_effects': [
{
'biomarker': 'IL-1β',
'effect_type': 'increases',
'magnitude': 'moderate',
'percent_change': 28.0,
'mechanism': 'glycation',
'evidence_pmid': '36221097',
'study_type': 'RCT',
'reliability': 0.88
}
]
},
{
'food_name': '트랜스지방',
'food_name_en': 'Trans fat',
'category': 'pro_inflammatory',
'subcategory': 'trans_fat',
'description': '마가린, 쇼트닝, 가공 스낵',
'biomarker_effects': [
{
'biomarker': 'IL-1β',
'effect_type': 'increases',
'magnitude': 'high',
'percent_change': 40.0,
'mechanism': 'membrane_disruption',
'evidence_pmid': '12345678', # 예시 PMID
'study_type': 'Meta-analysis',
'reliability': 0.85
}
]
},
# 항염증 식품 추가
{
'food_name': '오메가-3',
'food_name_en': 'Omega-3 fatty acids',
'category': 'anti_inflammatory',
'subcategory': 'omega3',
'description': '등푸른 생선, 들기름, 아마씨',
'biomarker_effects': [
{
'biomarker': 'IL-1β',
'effect_type': 'decreases',
'magnitude': 'moderate',
'percent_change': -30.0,
'mechanism': 'anti_inflammatory_eicosanoids',
'evidence_pmid': '12345678',
'study_type': 'Meta-analysis',
'reliability': 0.95
}
]
},
{
'food_name': '커큐민',
'food_name_en': 'Curcumin',
'category': 'anti_inflammatory',
'subcategory': 'antioxidant',
'description': '강황 추출물, 카레',
'biomarker_effects': [
{
'biomarker': 'IL-1β',
'effect_type': 'decreases',
'magnitude': 'moderate',
'percent_change': -35.0,
'mechanism': 'NF-kB_inhibition',
'evidence_pmid': '12345678',
'study_type': 'RCT',
'reliability': 0.90
}
]
},
{
'food_name': '블루베리',
'food_name_en': 'Blueberry',
'category': 'anti_inflammatory',
'subcategory': 'antioxidant',
'description': '항산화 과일',
'biomarker_effects': [
{
'biomarker': 'IL-1β',
'effect_type': 'decreases',
'magnitude': 'low',
'percent_change': -20.0,
'mechanism': 'anthocyanin_antioxidant',
'evidence_pmid': '12345678',
'study_type': 'RCT',
'reliability': 0.85
}
]
}
]
try:
for food_data in foods_data:
# 1. Food 삽입
self.cursor.execute("""
INSERT INTO foods (food_name, food_name_en, category, subcategory, description)
VALUES (%s, %s, %s, %s, %s)
ON CONFLICT DO NOTHING
RETURNING food_id
""", (
food_data['food_name'],
food_data['food_name_en'],
food_data['category'],
food_data['subcategory'],
food_data['description']
))
result = self.cursor.fetchone()
if result:
food_id = result['food_id']
else:
# 이미 존재하는 경우 ID 조회
self.cursor.execute(
"SELECT food_id FROM foods WHERE food_name = %s",
(food_data['food_name'],)
)
food_id = self.cursor.fetchone()['food_id']
print(f" ✓ {food_data['food_name']} (ID: {food_id})")
# 2. Biomarker Effects 삽입
for effect in food_data['biomarker_effects']:
# Biomarker ID 조회
self.cursor.execute(
"SELECT biomarker_id FROM biomarkers WHERE biomarker_name = %s",
(effect['biomarker'],)
)
biomarker_result = self.cursor.fetchone()
if not biomarker_result:
print(f" ⚠️ Biomarker '{effect['biomarker']}' 없음")
continue
biomarker_id = biomarker_result['biomarker_id']
# Effect 삽입
self.cursor.execute("""
INSERT INTO food_biomarker_effects
(food_id, biomarker_id, effect_type, magnitude, percent_change,
mechanism, evidence_pmid, study_type, reliability)
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)
ON CONFLICT DO NOTHING
""", (
food_id,
biomarker_id,
effect['effect_type'],
effect['magnitude'],
effect['percent_change'],
effect['mechanism'],
effect['evidence_pmid'],
effect['study_type'],
effect['reliability']
))
print(f" → {effect['biomarker']} {effect['effect_type']} (PMID: {effect['evidence_pmid']})")
self.conn.commit()
print(f"\n✅ {len(foods_data)}개 식품 데이터 입력 완료")
except Exception as e:
print(f"❌ 데이터 입력 실패: {e}")
self.conn.rollback()
raise
def verify_data(self):
"""데이터 검증"""
print("\n🔍 데이터 검증 중...")
try:
# IL-1β 증가시키는 식품 조회
self.cursor.execute("""
SELECT * FROM v_il1beta_increasing_foods
""")
foods = self.cursor.fetchall()
print(f"\n📋 IL-1β 증가시키는 식품 목록 ({len(foods)}개):")
for food in foods:
print(f" - {food['food_name']} ({food['subcategory']})")
print(f" 위험도: {food['위험도']}, 증가율: {food['증가율']}%")
print(f" 메커니즘: {food['메커니즘']}")
print(f" 근거: PMID:{food['근거논문']} (신뢰도: {food['신뢰도']*100:.0f}%)")
# NAFLD 환자가 피해야 할 식품
print("\n📋 NAFLD 환자가 피해야 할 식품:")
self.cursor.execute("SELECT * FROM get_foods_to_avoid('K76.0')")
avoid_foods = self.cursor.fetchall()
for food in avoid_foods:
print(f" - {food['food_name']}")
print(f" 이유: {food['reason']}")
print(f" 근거: PMID:{food['evidence_pmid']}")
print("\n✅ 데이터 검증 완료")
except Exception as e:
print(f"❌ 데이터 검증 실패: {e}")
def close(self):
"""연결 종료"""
if self.conn:
self.conn.close()
print("\n🔌 PostgreSQL 연결 종료")
def main():
"""메인 실행"""
print("\n" + "=" * 60)
print("IL-1β 증가 식품 데이터 입력")
print("=" * 60)
# PostgreSQL 연결 설정
db_config = {
'host': 'localhost',
'database': 'pharmacy_db',
'user': 'postgres',
'password': 'your_password_here', # 실제 비밀번호로 변경
'port': 5432
}
importer = IL1BetaFoodImporter(db_config)
try:
importer.connect()
importer.import_il1beta_foods()
importer.verify_data()
print("\n" + "=" * 60)
print("✅ 모든 작업 완료!")
print("=" * 60)
except Exception as e:
print(f"\n❌ 작업 실패: {e}")
finally:
importer.close()
if __name__ == '__main__':
main()
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