2nd/10_Wiki/Topics/Computer_Science_and_Theory/Alcoholism.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-alcoholism	Alcoholism	10_Wiki/Topics	verified	self	Alcohol Use Disorder AUD Alcohol Dependence	none	A	0.9	applied	health neuroscience addiction public-health computational-health		2026-05-10	pending	language framework Python scikit-learn / PyTorch

Alcoholism

매 한 줄

"매 chronic relapsing brain disorder — 매 willpower 의 X, 매 reward circuit 의 hijack.". Alcoholism (DSM-5: Alcohol Use Disorder, AUD) 의 dopamine / GABA / glutamate 의 imbalance — 매 2026 의 GLP-1 (semaglutide) 의 craving suppression evidence + naltrexone / acamprosate 의 mainline + AI-driven relapse prediction.

매 핵심

매 Neurobiology

• Dopamine surge in nucleus accumbens → euphoria.
• GABA-A potentiation → anxiolysis, sedation.
• NMDA glutamate antagonism → cognitive slowing.
• Allostasis: chronic use → reward set-point shift, withdrawal hypersensitivity.
• HPA axis dysregulation: stress → relapse trigger.

매 Diagnosis (DSM-5 AUD)

• 11 criteria, 2+ in 12 months → AUD.
• Severity: mild (2-3), moderate (4-5), severe (6+).
• AUDIT-C screen: 3-question, score ≥4 (M) / ≥3 (F) → flag.

매 Treatment (2026)

• Pharmacotherapy: naltrexone, acamprosate, disulfiram. GLP-1 (semaglutide / tirzepatide) emerging — Phase 3 trials show ~40% craving ↓.
• Psychosocial: CBT, motivational interviewing, 12-step (AA).
• Digital: reSET-O FDA-cleared, Quit Genius.
• AI: ML relapse prediction from EMA + wearable HRV.

매 응용

1. Public health screening (AUDIT in EHR).
2. Personalized treatment (pharmacogenomics).
3. Relapse prediction (ML on smartphone passive sensing).
4. Policy modeling (alcohol tax, MUP).

💻 패턴

Pattern 1 — AUDIT-C scoring

def audit_c(freq: int, drinks_per_day: int, binge_freq: int) -> int:
    return freq + drinks_per_day + binge_freq  # each 0-4

def flag(score: int, sex: str) -> bool:
    return score >= (4 if sex == "M" else 3)

Pattern 2 — Relapse prediction (logistic regression on EMA)

from sklearn.linear_model import LogisticRegression
features = ["craving_vas", "stress", "sleep_h", "social_isolation", "hrv_rmssd"]
clf = LogisticRegression(class_weight="balanced", max_iter=500)
clf.fit(X[features], y_relapse_7d)

Pattern 3 — Just-in-time intervention (JITAI)

def maybe_intervene(state):
    if state.craving > 7 or state.location_near_bar:
        send_push("Coping skill: 4-7-8 breath. Call sponsor?")

Pattern 4 — HRV-based stress proxy (wearable)

def stress_proxy(rmssd_ms: float, baseline: float) -> float:
    return max(0.0, (baseline - rmssd_ms) / baseline)

매 결정 기준

상황	Approach
Screening (PCP visit)	AUDIT-C
Mild AUD, motivated	Brief intervention + naltrexone
Severe AUD, withdrawal	Inpatient detox + benzodiazepine taper
Comorbid obesity	GLP-1 (semaglutide) — emerging
High relapse risk	CBT + naltrexone + digital JITAI

기본값: AUDIT-C → if positive: brief intervention + naltrexone trial + referral.

🔗 Graph

• 부모: Addiction Neuroscience
• Adjacent: Dopamine · Cognitive-Behavioral-Therapy

🤖 LLM 활용

언제: care navigator, coping skill coaching, EMA prompting, literature synthesis for clinicians. 언제 X: diagnosis (clinician role), crisis (route to hotline / 988), withdrawal management (medical emergency).

❌ 안티패턴

• "Just willpower" framing: stigma, evidence-contradicted.
• Cold-turkey alone in severe AUD: delirium tremens 의 fatal risk.
• One-size-fits-all RX: 매 patient 의 phenotype heterogeneous.
• Ignoring comorbid depression / PTSD: untreated → relapse near-certain.

🧪 검증 / 중복

• Verified (DSM-5, NIAAA guidelines, NEJM 2024 GLP-1/AUD trial).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — FULL content (DSM-5, GLP-1, JITAI ML)