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Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-20 23:52:15 +09:00

4.8 KiB

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

🤖 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