2nd/10_Wiki/Topics/Autism-Spectrum-Disorder.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-autism-spectrum-disorder	Autism Spectrum Disorder	10_Wiki/Topics	verified	self	ASD Autism 자폐스펙트럼장애	none	A	0.9	applied	neurodevelopment psychiatry autism dsm5		2026-05-10	pending	language framework python pandas/scikit-learn

Autism Spectrum Disorder

매 한 줄

"매 social communication deficits + restricted/repetitive behaviors 의 neurodevelopmental condition — 매 dimensional spectrum." 매 DSM-5 (2013) 의 single diagnosis (Asperger, PDD-NOS 의 merge) — 매 prevalence ~1/36 (CDC 2023) — 매 2026 에 polygenic + multimodal AI biomarkers, early intervention 의 efficacy 의 evidence-based.

매 핵심

매 DSM-5 criteria

• A. Social communication: 매 social-emotional reciprocity, nonverbal communication, relationships 의 deficits (all 3).
• B. Restricted/repetitive: 매 stereotyped behavior, insistence on sameness, restricted interests, sensory atypicality (≥2 of 4).
• C. Early developmental period (may not manifest until demands exceed capacity).
• D. Functional impairment.
• E. Not better explained by ID/global delay.

매 levels of support

• Level 1: requiring support.
• Level 2: requiring substantial support.
• Level 3: requiring very substantial support.

매 응용

1. Early screening (M-CHAT, ADOS-2).
2. Multimodal AI diagnosis (eye-tracking + voice + behavioral).
3. Personalized intervention (ABA, ESDM, JASPER).

💻 패턴

M-CHAT-R/F scoring

def mchat_rf_score(responses):
    # 20 items, certain answers indicate risk
    risk_answers = {
        1: "no", 2: "no", 3: "no", 4: "no", 5: "no",
        6: "no", 7: "no", 8: "no", 9: "no", 10: "no",
        11: "yes", 12: "yes", 13: "no", 14: "no", 15: "no",
        16: "no", 17: "no", 18: "yes", 19: "no", 20: "yes",
    }
    score = sum(1 for k, v in responses.items() if v == risk_answers[k])
    if score >= 8: return "high"
    if score >= 3: return "medium"  # follow-up interview
    return "low"

Eye-tracking social attention

import numpy as np

def social_attention_ratio(gaze_xy, face_aoi, object_aoi):
    in_face = points_in_aoi(gaze_xy, face_aoi).sum()
    in_obj = points_in_aoi(gaze_xy, object_aoi).sum()
    # Lower ratio observed in ASD vs TD
    return in_face / (in_face + in_obj + 1e-8)

Repetitive behavior detection (accelerometer)

from scipy.signal import find_peaks, welch

def stereotypy_score(accel_xyz, fs=50):
    mag = np.linalg.norm(accel_xyz, axis=1)
    f, psd = welch(mag, fs=fs, nperseg=512)
    # Stereotypies show narrow-band power 1-5 Hz
    band_power = psd[(f >= 1) & (f <= 5)].sum()
    total = psd.sum()
    return band_power / total

Voice prosody features

import librosa

def prosody_features(wav, sr):
    f0 = librosa.yin(wav, fmin=80, fmax=400, sr=sr)
    f0 = f0[f0 > 0]
    return {
        "f0_mean": np.mean(f0),
        "f0_std": np.std(f0),     # often atypical (mono- or sing-song)
        "f0_range": np.ptp(f0),
        "speaking_rate": estimate_rate(wav, sr),
    }

Polygenic risk score

def prs(genotype, weights):
    # weights: dict snp_id -> beta from GWAS
    score = 0.0
    for snp, dose in genotype.items():
        if snp in weights:
            score += dose * weights[snp]
    return score

매 결정 기준

상황	Approach
Toddler screen	M-CHAT-R/F at 18 + 24 months
Diagnostic confirm	ADOS-2 + ADI-R (gold standard)
Early intervention (<3y)	ESDM (Early Start Denver Model)
School-age	ABA, social skills groups, IEP
Co-occurring anxiety	CBT (modified), SSRI
Aggression / SIB	FBA + behavioral plan; meds last

기본값: 매 early screen → multidisciplinary eval → individualized plan.

🔗 Graph

• 부모: Neurodevelopmental Disorders · DSM-5
• 변형: Asperger Syndrome (legacy) · PDD-NOS (legacy)
• 응용: ABA · ESDM · ADOS-2
• Adjacent: ADHD · Intellectual Disability · Amygdala Hyperactivity · Sensory Processing Disorder

🤖 LLM 활용

언제: 매 caregiver psychoeducation, 매 IEP draft, 매 social-story generation. 언제 X: 매 diagnosis, 매 medication — 매 clinician 의.

❌ 안티패턴

• Single-snapshot diagnosis: 매 longitudinal observation 의 needed.
• One-size-fits-all therapy: 매 high heterogeneity — 매 individualization.
• MMR vaccine link: 매 debunked (Wakefield retracted 2010).
• Cure-focused framing: 매 neurodiversity perspective 의 respect.

🧪 검증 / 중복

• Verified (DSM-5-TR 2022, CDC ADDM 2023, Lord et al. Lancet 2018).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — DSM criteria + screening/biomarker patterns