/* rx-core.jsx — shared engine for the 처방전 분석 learning tool Exports to window: RX (theme), rxHelpers, RxForm, DrugPopup, Dropdown, SelectionBadge, RevealBlocks, AppTabs, SelectorBar, useRxSession */ const RX = { teal: '#1a6c5b', tealDark: '#12473b', tealTint: '#eef5f2', tealLine: '#bfdcd4', ink: '#181c1a', sub: '#5f6b66', faint: '#8a948f', line: '#d9dcd8', lineSoft: '#e8ebe7', paper: '#ffffff', shell: '#eef0ee', mask: '#c9cec9', amber: '#9a6a14', amberBg: '#fbf3e2', rose: '#9c3b54', roseBg: '#fbeef1', }; // ── helpers ────────────────────────────────────────────────── const rxHelpers = (() => { const DATA = window.RX_DATA, ORDER = window.RX_ORDER; const CONTEXT_MAP = window.CV_CONTEXT_DRUG_MAP || { entries: [] }; const DRUG_CLASSES = window.DRUG_CLASSES || []; const GENERAL_PRINCIPLES = window.GENERAL_PRINCIPLES || []; const DRUG_ROLE_MAP = window.DRUG_ROLE_MAP || []; const DOSE_COUNSELING_RULES = window.DOSE_COUNSELING_RULES || []; const OCR_SENSITIVE_RULES = window.OCR_SENSITIVE_RULES || { labels: [], regexes: [], rejectionMessage: '' }; const TOKEN_ALIAS_MAP = window.TOKEN_ALIAS_MAP || { generic: {}, brand: {}, class: {} }; const CV_DURATION_PRIORS = window.CV_DURATION_PRIORS || {}; const CV_COMBO_LABELS = (() => { const raw = window.CV_COMBO_LABELS; if (!raw || !Array.isArray(raw.labels)) return {}; const map = {}; raw.labels.forEach(l => { if (l.combo_key) map[l.combo_key] = l; }); return map; })(); const OCR_ATC_CLASS_MAP = { 'ARB+CCB 복합제': ['ARB (sartan)', 'DHP-CCB'], 'ARB+Thiazide계 복합제': ['ARB (sartan)', 'Thiazide계 및 유사체'], 'Micronized Flavonoid': ['정맥순환 개선'], 'HMG-CoA+Ezetimibe 복합제': ['HMG-CoA', '콜레스테롤 흡수억제제'], 'Statin+Ezetimibe 복합제': ['HMG-CoA', '콜레스테롤 흡수억제제'], }; const OCR_BB_ALIASES = { 'BB (선택성)': ['BB'], 'BB (혈관확장성)': ['BB'], 'BB (비선택성)': ['BB'], BB: ['BB (선택성)', 'BB (혈관확장성)', 'BB (비선택성)'], }; const DIFF_DX_DISEASE_GROUPS = { CV_AF: ['심방세동', '부정맥 치료제'], CV_Arrhythmia: ['부정맥(비-AF)', '부정맥 치료제'], CV_HyperT: ['고혈압', '고혈압 치료제'], CV_CCS: ['만성 관상동맥 증후군', '협심증 치료제'], CV_HF: ['심부전', '심부전 치료제'], }; const DIFF_DX_LABELS = { '고혈압 치료제': '고혈압', '협심증 치료제': '협심증', '부정맥 치료제': '부정맥', '심부전 치료제': '심부전', }; const diseaseList = (major, plan) => { const allowed = isUnlocked(plan) ? ['CV', 'PM', 'ED'] : ['CV']; return ORDER .filter(k => { const m = DATA[k].meta.major; if (!allowed.includes(m)) return false; if (major && major !== 'all' && m !== major) return false; return true; }) .map(k => ({ key: k, label: DATA[k].meta.diseaseLabel, major: DATA[k].meta.major, weight: DATA[k].meta.disease_match_pct || DATA[k].meta.disease_frequency_weight, actual_count: DATA[k].meta.actual_disease_count || 0, count: DATA[k].patterns.length, })) .sort((a, b) => b.actual_count - a.actual_count); }; const patternsFor = (key) => { const sorted = [...DATA[key].patterns].sort((a, b) => { const countA = a.actual_match_count || 0; const countB = b.actual_match_count || 0; if (countA !== countB) return countB - countA; return a.frequency_rank - b.frequency_rank; }); sorted.forEach((p, i) => { p.dynamic_rank = i + 1; p._diseaseKey = key; p._sourceFile = `${key}_rx.json`; }); return sorted; }; const getPattern = (key, idx) => patternsFor(key)[idx]; // weighted random across all diseases & patterns (optionally within one 진료과) const weightedRandom = (major, plan) => { const allowed = isUnlocked(plan) ? ['CV', 'PM', 'ED'] : ['CV']; const pool = []; for (const k of ORDER) { const m = DATA[k].meta.major; if (!allowed.includes(m)) continue; if (major && major !== 'all' && m !== major) continue; const w = DATA[k].meta.disease_frequency_weight || 1; DATA[k].patterns.forEach((p) => { const score = w * (1 / (p.frequency_rank || 1)); pool.push({ key: k, patternId: p.patternId, score }); }); } const total = pool.reduce((s, x) => s + x.score, 0); let r = Math.random() * total; for (const x of pool) { r -= x.score; if (r <= 0) return x; } return pool[0]; }; const idxOf = (key, patternId) => patternsFor(key).findIndex(p => p.patternId === patternId); const groupOf = (pattern, drug) => (pattern.drugGroups || []).find(g => g.id === drug.groupId); const labelOf = (key) => DATA[key] && DATA[key].meta.diseaseLabel; // patterns for the current scope. diseaseKey '__all' = mix every disease in the subspec. const scopedPatterns = (subspec, diseaseKey, plan) => { if (diseaseKey === '__all') { const out = []; diseaseList(subspec, plan).forEach(d => patternsFor(d.key).forEach(p => out.push({ pattern: p, key: d.key }))); return out; } return patternsFor(diseaseKey).map(p => ({ pattern: p, key: diseaseKey })); }; const scopedIdxOf = (subspec, diseaseKey, patternId, plan) => scopedPatterns(subspec, diseaseKey, plan).findIndex(x => x.pattern.patternId === patternId); // **bold** + \n → react nodes const fmt = (str) => { if (!str) return null; return str.split('\n').map((line, li) => { const parts = line.split(/(\*\*[^*]+\*\*)/g).filter(Boolean); return ( {li > 0 &&
} {parts.map((p, i) => p.startsWith('**') && p.endsWith('**') ? {p.slice(2, -2)} : {p} )} ); }); }; // split cautions string into list items (lines starting with ①②③ or ④⑤) const cautionItems = (str) => { if (!str) return []; return str.split('\n').map(s => s.trim()).filter(Boolean).map(line => { const m = line.match(/^([①②③④⑤⑥⑦⑧⑨⑩])\s*/); return { marker: m ? m[1] : '•', text: m ? line.slice(m[0].length) : line }; }); }; const normContextText = (text) => String(text || '') .replace(/\s*\d+[\d.]*\s*(mg|mcg|μg|g|ml|mL|IU|U|정|캡슐|시럽|주|%)?/gi, '') .trim() .toLowerCase(); const entryMatchesPatternDrug = (entry, pattern) => { const needles = [...(entry.brand || []), ...(entry.generic || [])] .map(normContextText) .filter(Boolean); const haystacks = (pattern.drugs || []).flatMap(d => [ normContextText(d.brand), normContextText(d.ingredient), normContextText(d.ingredient_en), normContextText(d.generic), ]).filter(Boolean); return needles.some(needle => haystacks.some(haystack => needle === haystack || needle.includes(haystack) || haystack.includes(needle))); }; const contextNotesForPattern = (pattern) => { if (!pattern) return []; if (Array.isArray(pattern.context_notes)) return pattern.context_notes; const entries = Array.isArray(CONTEXT_MAP.entries) ? CONTEXT_MAP.entries : []; const notes = []; const seen = new Set(); entries.forEach(entry => { const policy = entry.anchorPolicy || {}; if (policy.standaloneMatch === true) return; if (!entryMatchesPatternDrug(entry, pattern)) return; const allowedFiles = new Set(policy.allowedPatternFiles || []); const allowedDiseases = new Set(policy.allowedDiseaseIds || []); const diseaseIds = new Set([ pattern._diseaseKey, pattern._sourceFile, ...(pattern.diseases || []), ].filter(Boolean)); const anchoredByFile = allowedFiles.has(pattern._sourceFile); const anchoredByDisease = [...allowedDiseases].some(d => diseaseIds.has(d)); if (entry.requiresAnchor !== false && !anchoredByFile && !anchoredByDisease) return; if (seen.has(entry.contextId)) return; seen.add(entry.contextId); notes.push({ contextId: entry.contextId, targetDisease: entry.targetDisease, contextType: entry.contextType, clinicalContext: entry.clinicalContext || '', interpretation: entry.interpretation || '', confidence: entry.confidence || 'standard', }); }); return notes; }; const normDrug = (str) => String(str || '') .replace(/\s*\d+[\d.]*\s*(mg|mcg|μg|ug|g|ml|mL|IU|U|정|캡슐|시럽|주|%)?/gi, '') .replace(/[()\[\]{}·,._\-\/]/g, '') .replace(/\s+/g, '') .trim() .toLowerCase(); const ocrTextFromDrug = (drug) => { if (typeof drug === 'string') return drug; return drug && (drug.rawName || drug.name || drug.brand || drug.generic || drug.ingredient || ''); }; const normalizeOcrProduct = (str) => String(str || '') .replace(/^\s*\d+\s*[.)-]?\s*/, '') .replace(/밀리그램/gi, 'mg') .replace(/마이크로그램|㎍/gi, 'mcg') .replace(/㎎/gi, 'mg') .replace(/^[\s★☆□■●○ⓟⓅPp]+/g, '') .replace(/\([^)]*\)/g, '') .replace(/(\d+(?:\.\d+)?(?:\/\d+(?:\.\d+)?)?\s*(?:mg|mcg|μg|ug|g|ml|mL|IU|U|%))(?:[-_].*)$/i, '$1') .replace(/[-_](?:EL|EK)(?:[-_]?\d+일?)?$/i, '') .replace(/[-_](?:노랑|황색|갈색|백색|흰색|분홍|연두|녹색|초록|주황|빨강|파랑|청색|투명|흑색|검정|보라|원형|장방형|타원형|타원|팔각형|육각형|삼각형|마름모|사각형)[a-zA-Z0-9가-힣]*$/i, '') .replace(/[-_](?:\d+일?|\d+)$/i, '') .replace(/\s*\d+[\d.]*\s*(mg|mcg|μg|ug|g|ml|mL|IU|U|정|캡슐|캅셀|시럽|주|%)?/gi, '') .replace(/[()\[\]{}·,._\-\/]/g, '') .replace(/\s+/g, '') .trim() .toLowerCase(); const isCvDrugRow = (row) => { if (!row) return false; if (row.major_code === 'CV' || row.major === '심혈관질환') return true; return (row.cross_refs || []).some(ref => ref && (ref.major_code === 'CV' || ref.major === 'CV' || ref.major === '심혈관질환')); }; const productIndex = (() => { const items = []; const seen = new Set(); DRUG_CLASSES.forEach(row => { const add = (name, dose, source) => { const norm = normalizeOcrProduct(name); if (!norm || norm.length < 2) return; const key = `${norm}|${row.generic || ''}|${dose || ''}`; if (seen.has(key)) return; seen.add(key); items.push({ norm, name, dose: dose || '', source, row, isCv: isCvDrugRow(row) }); }; add(row.brand, '', 'brand'); (row.strengths || []).forEach(strength => { add(strength.official_name, strength.dose, 'official_name'); (strength.brand_aliases || []).forEach(alias => add(alias, strength.dose, 'brand_alias')); }); }); // non-CV 인덱스 편입 — isCv:false로 CV 분석과 완전 분리 const existingNormSet = new Set(items.map(i => i.norm)); const NON_CV = window.NON_CV_DRUG_INDEX || {}; Object.entries(NON_CV).forEach(([, info]) => { const brandsToAdd = (info.brands && info.brands.length > 0) ? info.brands : []; brandsToAdd.forEach(brand => { const rtNorm = normalizeOcrProduct(brand); if (!rtNorm || rtNorm.length < 2) return; if (existingNormSet.has(rtNorm)) return; existingNormSet.add(rtNorm); items.push({ norm: rtNorm, name: brand, dose: '', source: 'non_cv_index', isCv: false, row: { brand, generic: (info.generics || []).join(', '), drug_class: info.non_cv_class || '', major_code: info.major_code || 'NON_CV', major: info.major || info.category || '', }, }); }); }); return items; })(); // Sonnet 텍스트 교정에 넘길 실제 상품명 목록 (brand/official_name, deduped) const productBrandList = (() => { const seen = new Set(); const out = []; productIndex.forEach(item => { if (item.source === 'brand_alias') return; const name = String(item.name || '').trim(); if (!name || seen.has(name)) return; seen.add(name); out.push(name); }); return out; })(); const simpleRatio = (a, b) => { if (!a || !b) return 0; const maxLen = Math.max(a.length, b.length); if (!maxLen) return 1; if (a === b) return 1; if (a.includes(b) || b.includes(a)) return Math.min(1, (Math.min(a.length, b.length) / maxLen) + 0.20); return Math.max( 0, 1 - (levenshtein(a, b) / maxLen), jamoSimilarity(a, b), brandSimilarity(a, b) ); }; const extractBrandOnly = (str) => normalizeOcrProduct(str) .replace(/[0-9a-zA-Z./]+/g, '') .replace(/정|캡슐|캅셀|밀리그램|시럽|주/g, '') .trim(); const hangulToJamo = (str) => { const choseong = ['ㄱ', 'ㄲ', 'ㄴ', 'ㄷ', 'ㄸ', 'ㄹ', 'ㅁ', 'ㅂ', 'ㅃ', 'ㅅ', 'ㅆ', 'ㅇ', 'ㅈ', 'ㅉ', 'ㅊ', 'ㅋ', 'ㅌ', 'ㅍ', 'ㅎ']; const jungseong = ['ㅏ', 'ㅐ', 'ㅑ', 'ㅒ', 'ㅓ', 'ㅔ', 'ㅕ', 'ㅖ', 'ㅗ', 'ㅘ', 'ㅙ', 'ㅚ', 'ㅛ', 'ㅜ', 'ㅝ', 'ㅞ', 'ㅟ', 'ㅠ', 'ㅡ', 'ㅢ', 'ㅣ']; const jongseong = ['', 'ㄱ', 'ㄲ', 'ㄳ', 'ㄴ', 'ㄵ', 'ㄶ', 'ㄷ', 'ㄹ', 'ㄺ', 'ㄻ', 'ㄼ', 'ㄽ', 'ㄾ', 'ㄿ', 'ㅀ', 'ㅁ', 'ㅂ', 'ㅄ', 'ㅅ', 'ㅆ', 'ㅇ', 'ㅈ', 'ㅊ', 'ㅋ', 'ㅌ', 'ㅍ', 'ㅎ']; return String(str || '').split('').map(ch => { const code = ch.charCodeAt(0); if (code < 0xac00 || code > 0xd7a3) return ch; const offset = code - 0xac00; const cho = Math.floor(offset / 588); const jung = Math.floor((offset % 588) / 28); const jong = offset % 28; return `${choseong[cho]}${jungseong[jung]}${jongseong[jong]}`; }).join(''); }; const jamoSimilarity = (a, b) => { const ja = hangulToJamo(a); const jb = hangulToJamo(b); const maxLen = Math.max(ja.length, jb.length); if (!maxLen) return 1; return Math.max(0, 1 - (levenshtein(ja, jb) / maxLen)); }; const brandSimilarity = (a, b) => { const ba = extractBrandOnly(a); const bb = extractBrandOnly(b); if (!ba || !bb) return 0; const syllables = s => [...s].filter(c => c >= '가' && c <= '힣'); const sa = syllables(ba), sb = syllables(bb); const syllSim = (sa.length && sb.length) ? (1 - levenshtein(sa.join(''), sb.join('')) / Math.max(sa.length, sb.length)) * 1.3 : 0; return Math.min(0.95, Math.max(jamoSimilarity(ba, bb), syllSim)) * 0.98; }; const confidenceFromScore = (score) => { if (score >= 0.94) return 'high'; if (score >= 0.86) return 'medium'; return 'low'; }; // rawName과 유사한 상위 N개 상품명 반환 — Sonnet 힌트용 const fuzzyCandidates = (rawName, limit = 15) => { if (!rawName) return []; const norm = normalizeOcrProduct(rawName); const seen = new Set(); return productIndex .map(item => ({ name: item.name, score: Math.max(simpleRatio(norm, item.norm), jamoSimilarity(norm, item.norm)) })) .sort((a, b) => b.score - a.score) .filter(x => x.score > 0.1 && x.name && !seen.has(x.name) && seen.add(x.name)) .slice(0, limit) .map(x => x.name); }; const extractDoseText = (drug, candidate) => { const parts = [ drug && drug.dosage, drug && drug.dose, drug && drug.name, candidate && candidate.dose, ].filter(Boolean).map(String); const joined = parts.join(' '); const match = joined.match(/(\d+[\d.]*)\s*(mg|mcg|μg|ug|g|ml|mL|IU|U)/i); if (!match) return candidate && candidate.dose ? candidate.dose : ''; return `${match[1]}${match[2].replace(/μg|ug/i, 'mcg')}`; }; const doseConfidenceFor = (drug, candidate) => { const text = [drug && drug.dosage, drug && drug.dose, drug && drug.name].filter(Boolean).join(' '); const hasDose = /\d+[\d.]*\s*(mg|mcg|μg|ug|g|ml|mL|IU|U)/i.test(text); if (hasDose) return 'high'; return candidate && candidate.dose ? 'medium' : 'low'; }; const expandBasketTokens = (row, brand) => { const tokens = new Set(); const drugClass = row && row.drug_class; const generic = row && row.generic; if (drugClass) { tokens.add(drugClass); (OCR_ATC_CLASS_MAP[drugClass] || []).forEach(t => tokens.add(t)); (OCR_BB_ALIASES[drugClass] || []).forEach(t => tokens.add(t)); ((TOKEN_ALIAS_MAP.class || {})[drugClass] || []).forEach(t => tokens.add(t)); } if (generic) { ((TOKEN_ALIAS_MAP.generic || {})[String(generic).trim().toLowerCase()] || []).forEach(t => tokens.add(t)); } if (brand) { ((TOKEN_ALIAS_MAP.brand || {})[String(brand).trim()] || []).forEach(t => tokens.add(t)); } const expanded = new Set(tokens); tokens.forEach(token => (OCR_BB_ALIASES[token] || []).forEach(t => expanded.add(t))); return [...expanded]; }; const normalizationTypeForHit = (norm, hit) => { if (!hit || !hit.item) return 'unknown'; if (norm === hit.item.norm) return hit.item.source === 'brand' ? 'exact' : 'alias'; return 'fuzzy_correction'; }; const matchOcrDrugToProducts = (drug) => { const raw = ocrTextFromDrug(drug); const norm = normalizeOcrProduct(raw); if (!norm) return null; const candidates = productIndex .map(item => ({ item, score: simpleRatio(norm, item.norm) })) .filter(hit => hit.score >= 0.82) .sort((a, b) => b.score - a.score) .slice(0, 3) .map(hit => { const row = hit.item.row; const dose = extractDoseText(drug, hit.item); const doseConfidence = doseConfidenceFor(drug, hit.item); const normalizationType = normalizationTypeForHit(norm, hit); return { brand: hit.item.name || row.brand, displayName: hit.item.name || row.brand, representativeBrand: row.brand || hit.item.name, matchedProductName: hit.item.name, original_name: raw, generic: row.generic || raw, ingredient: row.generic || raw, drug_class: row.drug_class || '', major: row.major || '', major_code: row.major_code || '', disease: row.disease || '', isCv: hit.item.isCv, score: hit.score, ingredientConfidence: confidenceFromScore(hit.score), normalizationType, corrected: normalizationType === 'fuzzy_correction', dose, dosage: drug && drug.dosage, frequency: drug && drug.frequency, duration: drug && drug.duration, usage: drug && drug.usage, days: drug && drug.duration, timing: drug && drug.usage, doseConfidence, aiCorrection: drug && drug.aiCorrection, basket_tokens: hit.item.isCv ? expandBasketTokens(row, hit.item.name) : [], row, }; }); if (!candidates.length) return null; return { original: drug, original_name: raw, candidates, best: candidates[0], matched: candidates[0].ingredientConfidence !== 'low', }; }; const roleForToken = (disease, token, drugClass) => DRUG_ROLE_MAP.find(role => role.targetDisease === disease && (role.basket_token === token || role.drug_class === token || role.drug_class === drugClass)); const cvRoleKeys = [...new Set( DRUG_ROLE_MAP .map(role => role && role.targetDisease) .filter(key => key && key.startsWith('CV_')) )]; const roleWeight = (role) => { if (!role) return 0; if (role.confidence === 'high') return 1; if (role.confidence === 'medium') return 0.65; return 0.35; }; const signatureScore = (pattern, tokens) => { const include = (pattern.signature && pattern.signature.include) || []; const exclude = (pattern.signature && pattern.signature.exclude) || []; if (!include.length) return 0; if (exclude.some(token => tokens.has(token))) return 0; const matched = include.filter(token => tokens.has(token)).length; return matched / include.length; }; // ── duration/dose 신호 보너스 (prescription/engine/ocr_adapter.py와 패리티) ── const RX_DUR_RE = /(\d+)\s*(?:일분|일치|일|Days|D)(?![0-9가-힣])(?!\s*\d+\s*(?:회|times?))/i; const durationDaysForDrug = (drug) => { const raw = drug && drug.duration; if (raw) { const m = String(raw).match(/\d+/); if (m) return parseInt(m[0], 10); } const m = String((drug && (drug.original_name || drug.name)) || '').match(RX_DUR_RE); return m ? parseInt(m[1], 10) : null; }; const representativeDuration = (drugs) => { const days = drugs.map(durationDaysForDrug).filter(Boolean); if (!days.length) return null; const counts = {}; days.forEach(d => { counts[d] = (counts[d] || 0) + 1; }); const top = Math.max(...Object.values(counts)); return Math.max(...Object.keys(counts).filter(d => counts[d] === top).map(Number)); }; const durationSignalBonuses = (diseaseKey, daysHint, repDuration) => { if (repDuration == null) return []; const bonuses = []; const chronic = CV_DURATION_PRIORS.chronicDiseases || []; if (repDuration >= (CV_DURATION_PRIORS.chronicMinDays || 28) && chronic.includes(diseaseKey)) { bonuses.push({ type: 'duration_chronic', value: CV_DURATION_PRIORS.chronicBonus || 0.05 }); } const tol = CV_DURATION_PRIORS.daysHintTolerance || 3; if (daysHint != null && Math.abs(repDuration - daysHint) <= tol) { bonuses.push({ type: 'duration_days_hint', value: CV_DURATION_PRIORS.daysHintBonus || 0.05 }); } return bonuses; }; const doseSignalBonuses = (diseaseKey, drugs) => { const bonuses = []; DOSE_COUNSELING_RULES.forEach(rule => { (rule.doseRules || []).forEach(doseRule => { const hint = doseRule.diseaseHint; if (!hint || hint.disease !== diseaseKey) return; const pattern = String(doseRule.dosePattern || '').replace(/\s+/g, '').toLowerCase(); const ruleGeneric = String(rule.generic || '').trim().toLowerCase(); const hit = drugs.some(drug => { if (!drug || drug.doseConfidence !== 'high') return false; if (!ruleGeneric || !genericComponents(drug.generic).includes(ruleGeneric)) return false; const doseText = String(drug.dose || drug.dosage || '').replace(/\s+/g, '').toLowerCase(); return pattern && doseText.includes(pattern); }); if (hit) bonuses.push({ type: 'dose_hint', value: Math.min(Number(hint.bonus || 0.03), 0.03) }); }); }); return bonuses; }; const rankCvCandidateContexts = (matchedCvDrugs) => { if (!matchedCvDrugs.length) return []; const allTokens = new Set(matchedCvDrugs.flatMap(drug => drug.basket_tokens || [])); const repDuration = representativeDuration(matchedCvDrugs); const bonusCap = CV_DURATION_PRIORS.bonusCap || 0.1; const byDisease = {}; ORDER.filter(key => key.startsWith('CV_') && DATA[key]).forEach(key => { const explained = new Set(); const roles = []; let scoreSum = 0; matchedCvDrugs.forEach(drug => { let bestRole = null; (drug.basket_tokens || []).forEach(token => { const role = roleForToken(key, token, drug.drug_class); if (role && roleWeight(role) > roleWeight(bestRole)) bestRole = role; }); if (bestRole) { explained.add(drug.generic || drug.brand); roles.push({ brand: drug.brand, generic: drug.generic, token: bestRole.basket_token || drug.drug_class, roleLabel: bestRole.roleLabel, roleDescription: bestRole.roleDescription, confidence: bestRole.confidence || 'standard', }); scoreSum += roleWeight(bestRole); } }); let bestPattern = null; let bestPatternScore = 0; (DATA[key].patterns || []).forEach(pattern => { const score = signatureScore(pattern, allTokens); if (score > bestPatternScore) { bestPatternScore = score; bestPattern = pattern; } }); if (explained.size || bestPatternScore > 0) { const coverage = explained.size / Math.max(matchedCvDrugs.length, 1); const roleScore = scoreSum / Math.max(matchedCvDrugs.length, 1); const score = Math.min(1, (coverage * 0.45) + (roleScore * 0.35) + (bestPatternScore * 0.2)); const daysHint = bestPattern && bestPattern.signature && bestPattern.signature.disambiguate ? bestPattern.signature.disambiguate.days_hint : null; const signalBonuses = durationSignalBonuses(key, daysHint, repDuration) .concat(doseSignalBonuses(key, matchedCvDrugs)); const bonusTotal = Math.min(signalBonuses.reduce((s, b) => s + b.value, 0), bonusCap); byDisease[key] = { diseaseKey: key, diseaseLabel: labelOf(key) || key, score: score + bonusTotal, baseScore: score, signalBonuses, explainedDrugs: [...explained].sort(), roles, bestPattern: bestPattern ? { patternId: bestPattern.patternId, title: bestPattern.title, aiInterpretation: bestPattern.aiInterpretation || '', } : null, }; } }); return Object.values(byDisease) .sort((a, b) => b.score - a.score) .slice(0, 6); }; const genericComponents = (generic) => String(generic || '') .split('+') .map(part => part.trim().toLowerCase()) .filter(Boolean); const doseCounselingForDrug = (drug) => { if (!drug || drug.doseConfidence !== 'high') return []; const doseText = String(drug.dose || drug.dosage || '').replace(/\s+/g, '').toLowerCase(); if (!doseText) return []; const genericParts = genericComponents(drug.generic); const notes = []; DOSE_COUNSELING_RULES.forEach(rule => { const ruleGeneric = String(rule.generic || '').trim().toLowerCase(); const genericHit = ruleGeneric && genericParts.includes(ruleGeneric); const classHit = rule.drug_class && rule.drug_class === drug.drug_class; if (!genericHit && !classHit) return; (rule.doseRules || []).forEach(doseRule => { const pattern = String(doseRule.dosePattern || '').replace(/\s+/g, '').toLowerCase(); if (!pattern || !doseText.includes(pattern)) return; notes.push({ ruleId: rule.ruleId, brand: drug.brand, generic: drug.generic, meaning: doseRule.meaning, counseling: doseRule.counseling, }); }); }); return notes; }; const qualityFeedbackPreflight = (text) => { const payload = String(text || ''); const labelHit = (OCR_SENSITIVE_RULES.labels || []).find(label => payload.includes(label)); let regexHit = null; for (const pattern of (OCR_SENSITIVE_RULES.regexes || [])) { try { const re = new RegExp(pattern); if (re.test(payload)) { regexHit = pattern; break; } } catch (e) { // Ignore invalid local policy regexes; source JSON is validated in Python tests. } } const blocked = Boolean(labelHit || regexHit); return { allowed: !blocked, reason: labelHit ? `label:${labelHit}` : (regexHit ? `regex:${regexHit}` : ''), message: blocked ? (OCR_SENSITIVE_RULES.rejectionMessage || '사진에 민감정보가 포함되어 전송이 거절되었습니다.') : '', }; }; const safeOcrText = (value, max = 120) => String(value || '').slice(0, max); const ocrDrugLabel = (drug) => [ drug && (drug.rawName || drug.original_name || drug.name || drug.brand || drug.displayName), drug && (drug.dosage || drug.dose), ].filter(Boolean).join(' '); const hasMedicationEvidence = (drug) => { const text = ocrDrugLabel(drug); if (!text.trim()) return false; if (drug && (drug.dosage || drug.dose || drug.frequency || drug.duration || drug.usage)) return true; return /(\d+(?:\.\d+)?\s*(?:mg|㎎|밀리그램|g|mcg|μg|ug|ml|mL|%|IU)|\d+\/\d+|정|캡슐|캅셀|시럽|액|서방|패치|패취|스프레이|흡입|점안|점이|연고|크림)/i.test(text); }; const sanitizeTopCandidates = (items, limit = 3) => (items || []).slice(0, limit).map(c => ({ brand: safeOcrText(c.brand || c.displayName, 80), generic: safeOcrText(c.generic, 80), drug_class: safeOcrText(c.drug_class, 60), score: c.score, })); const sanitizeMatchedOcrDrug = (drug) => ({ displayName: safeOcrText(drug.displayName || drug.brand, 100), brand: safeOcrText(drug.brand || drug.displayName, 100), representativeBrand: safeOcrText(drug.representativeBrand, 100), original_name: safeOcrText(drug.original_name, 120), generic: safeOcrText(drug.generic, 100), drug_class: safeOcrText(drug.drug_class, 80), dose: safeOcrText(drug.dose || drug.dosage, 40), dosage: safeOcrText(drug.dosage || drug.dose, 40), frequency: safeOcrText(drug.frequency, 40), duration: safeOcrText(drug.duration, 40), usage: safeOcrText(drug.usage, 80), doseConfidence: safeOcrText(drug.doseConfidence, 20), ingredientConfidence: safeOcrText(drug.ingredientConfidence, 20), corrected: Boolean(drug.corrected), normalizationType: safeOcrText(drug.normalizationType, 40), isCv: Boolean(drug.isCv), aiCorrection: drug.aiCorrection ? { correctedBrand: safeOcrText(drug.aiCorrection.correctedBrand, 100), correctedGeneric: safeOcrText(drug.aiCorrection.correctedGeneric, 100), confidence: safeOcrText(drug.aiCorrection.confidence, 20), failureStage: safeOcrText(drug.aiCorrection.failureStage, 40), reason: safeOcrText(drug.aiCorrection.reason, 160), modelUsed: safeOcrText(drug.aiCorrection.modelUsed, 60), escalated: Boolean(drug.aiCorrection.escalated), } : null, basket_tokens: (drug.basket_tokens || []).slice(0, 12).map(t => safeOcrText(t, 60)), topCandidates: sanitizeTopCandidates(drug.topCandidates), }); const sanitizeRawOcrDrug = (drug) => ({ rawName: safeOcrText(drug.rawName || drug.original_name || drug.name, 120), name: safeOcrText(drug.name || drug.rawName || drug.original_name, 120), dosage: safeOcrText(drug.dosage || drug.dose, 40), frequency: safeOcrText(drug.frequency, 40), duration: safeOcrText(drug.duration, 40), usage: safeOcrText(drug.usage, 80), }); const sanitizeUnmatchedOcrDrug = (drug) => ({ original_name: safeOcrText(drug.original_name || drug.rawName || drug.name, 120), dosage: safeOcrText(drug.dosage || drug.dose, 40), frequency: safeOcrText(drug.frequency, 40), duration: safeOcrText(drug.duration, 40), usage: safeOcrText(drug.usage, 80), candidates: sanitizeTopCandidates(drug.candidates, 3), }); const sanitizeOcrAnalysisForStorage = (analysis = {}) => ({ rawDrugs: (analysis.rawDrugs || []) .filter(hasMedicationEvidence) .map(sanitizeRawOcrDrug), matchedCvDrugs: (analysis.matchedCvDrugs || []).map(sanitizeMatchedOcrDrug), recognizedNonCvDrugs: (analysis.recognizedNonCvDrugs || []).map(sanitizeMatchedOcrDrug), unmatchedDrugs: (analysis.unmatchedDrugs || []) .filter(hasMedicationEvidence) .map(sanitizeUnmatchedOcrDrug), candidateContexts: (analysis.candidateContexts || []).slice(0, 5).map(c => ({ diseaseKey: safeOcrText(c.diseaseKey, 40), diseaseLabel: safeOcrText(c.diseaseLabel, 60), score: c.score, likelihood: safeOcrText(c.likelihood, 30), reason: safeOcrText(c.reason, 180), explainedDrugs: (c.explainedDrugs || []).slice(0, 20).map(d => safeOcrText(d, 80)), unexplainedCvDrugs: (c.unexplainedCvDrugs || []).slice(0, 20).map(d => safeOcrText(d, 80)), drugRoles: (c.drugRoles || []).slice(0, 20).map(r => ({ brand: safeOcrText(r.brand, 80), generic: safeOcrText(r.generic, 80), roleLabel: safeOcrText(r.roleLabel, 80), roleDescription: safeOcrText(r.roleDescription, 180), confidence: safeOcrText(r.confidence, 20), })), bestPattern: c.bestPattern ? { patternId: safeOcrText(c.bestPattern.patternId, 60), title: safeOcrText(c.bestPattern.title, 120), } : null, })), combinationScenarios: (analysis.combinationScenarios || []).slice(0, 5).map(s => ({ score: s.score, diseases: (s.diseases || []).slice(0, 6).map(d => ({ diseaseKey: safeOcrText(d.diseaseKey, 40), diseaseLabel: safeOcrText(d.diseaseLabel, 60), drugs: (d.drugs || []).slice(0, 20).map(dr => ({ brand: safeOcrText(dr.brand, 80), generic: safeOcrText(dr.generic, 80), roleLabel: safeOcrText(dr.roleLabel, 80), roleDescription: safeOcrText(dr.roleDescription, 180), })), })), })), doseCounselingNotes: (analysis.doseCounselingNotes || []).slice(0, 10).map(n => ({ brand: safeOcrText(n.brand, 80), generic: safeOcrText(n.generic, 80), meaning: safeOcrText(n.meaning, 120), counseling: safeOcrText(n.counseling, 180), })), }); const levenshtein = (a, b) => { if (!a) return b ? b.length : 0; if (!b) return a.length; const matrix = Array.from({ length: a.length + 1 }, () => []); for (let i = 0; i <= a.length; i++) matrix[i][0] = i; for (let j = 0; j <= b.length; j++) matrix[0][j] = j; for (let i = 1; i <= a.length; i++) { for (let j = 1; j <= b.length; j++) { const cost = a[i - 1] === b[j - 1] ? 0 : 1; matrix[i][j] = Math.min( matrix[i - 1][j] + 1, matrix[i][j - 1] + 1, matrix[i - 1][j - 1] + cost ); } } return matrix[a.length][b.length]; }; const lookupDrugRow = (drug) => { if (!drug) return null; const brandNorm = normDrug(drug.brand); if (brandNorm) { const hit = DRUG_CLASSES.find(row => normDrug(row.brand) === brandNorm); if (hit) return hit; } const genericNeedles = [ drug.generic, drug.ingredient, drug.ingredient_en, ].map(normDrug).filter(Boolean); for (const needle of genericNeedles) { const hit = DRUG_CLASSES.find(row => normDrug(row.generic) === needle); if (hit) return hit; } // Phase 2: Fuzzy Matching (Levenshtein Distance <= 2) if (brandNorm && brandNorm.length >= 2) { let bestMatch = null; let minDistance = 999; for (const row of DRUG_CLASSES) { const rowBrandNorm = normDrug(row.brand); if (!rowBrandNorm) continue; // Fast optimization: length difference constraint if (Math.abs(rowBrandNorm.length - brandNorm.length) > 2) continue; const dist = levenshtein(brandNorm, rowBrandNorm); if (dist <= 2 && dist < minDistance) { minDistance = dist; bestMatch = { ...row, corrected: true, original_brand: drug.brand }; } } if (bestMatch) return bestMatch; } if (drug.drug_class) { const hits = DRUG_CLASSES.filter(row => row.drug_class === drug.drug_class && row.is_reference === true); if (hits.length === 1) return hits[0]; } return null; }; const diseaseTermsForPattern = (pattern) => { const terms = new Set(pattern.diseases || []); const diseaseKey = pattern._diseaseKey || ''; const groupTerms = DIFF_DX_DISEASE_GROUPS[diseaseKey] || []; groupTerms.forEach(term => terms.add(term)); return terms; }; const principleForDisease = (disease) => GENERAL_PRINCIPLES.find(principle => Array.isArray(principle.targets) && principle.targets.some(target => target && target.disease === disease)); const differentialDxForPattern = (pattern) => { if (!pattern || !Array.isArray(pattern.drugs) || !DRUG_CLASSES.length) return []; const currentTerms = diseaseTermsForPattern(pattern); const diseaseMap = {}; pattern.drugs.forEach(drug => { const row = lookupDrugRow(drug); if (!row) return; if (!Array.isArray(row.cross_refs) || row.cross_refs.length === 0) return; const candidates = []; if (row.disease) { candidates.push({ disease: row.disease, drug_class: row.drug_class || drug.drug_class || '', }); } row.cross_refs.forEach(ref => { if (ref && ref.disease) candidates.push(ref); }); candidates.forEach(ref => { if (!ref || !ref.disease) return; if (currentTerms.has(ref.disease)) return; if ((pattern.diseases || []).includes(ref.disease)) return; if (!diseaseMap[ref.disease]) { const principle = principleForDisease(ref.disease); diseaseMap[ref.disease] = { disease: ref.disease, label: DIFF_DX_LABELS[ref.disease] || ref.disease, drugs: [], summary: principle ? (principle.summary || '') : '', }; } const duplicate = diseaseMap[ref.disease].drugs.some(item => item.generic === row.generic && item.brand === row.brand && item.role === (ref.drug_class || drug.drug_class || '')); if (!duplicate) { diseaseMap[ref.disease].drugs.push({ generic: row.generic || drug.ingredient || drug.generic || '', brand: row.brand || drug.brand || '', role: ref.drug_class || drug.drug_class || '', }); } }); }); return Object.values(diseaseMap); }; // B-1. cross_refs 한글 질환명 → CV_* 키 역매핑 const NAME_TO_DISEASE_KEY = (() => { const map = {}; // DIFF_DX_DISEASE_GROUPS 기반 Object.entries(DIFF_DX_DISEASE_GROUPS).forEach(([key, aliases]) => { aliases.forEach(alias => { map[alias] = key; }); }); // rx meta diseaseLabel 기반 (DATA가 로딩된 경우) if (DATA) { Object.keys(DATA).filter(k => k.startsWith('CV_')).forEach(key => { const label = DATA[key] && DATA[key].meta && DATA[key].meta.diseaseLabel; if (label) map[label] = key; }); } // cross_refs에서 자주 쓰는 "...치료제" 형태 추가 매핑 const extra = { '고지혈증 치료제': 'CV_DysL', '이상지질혈증': 'CV_DysL', '이상지질혈증 치료제': 'CV_DysL', '급성관상동맥증후군': 'CV_ACS', '급성관상동맥증후군 치료제': 'CV_ACS', '뇌졸중': 'CV_Stroke', '뇌졸중 치료제': 'CV_Stroke', '말초동맥질환': 'CV_PAD', '말초동맥질환 치료제': 'CV_PAD', '심방세동 치료제': 'CV_AF', '폐동맥고혈압': 'CV_PH', '폐동맥고혈압 치료제': 'CV_PH', '정맥질환': 'CV_VV', '정맥질환 치료제': 'CV_VV', }; return { ...map, ...extra }; })(); // B-2. 단일 약물의 후보 CV 질환 목록 산출 const drugDiseaseCandidates = (drug) => { const result = {}; const addCandidate = (diseaseKey, roleLabel, roleDescription, weight) => { if (!diseaseKey || !diseaseKey.startsWith('CV_')) return; if (!result[diseaseKey] || result[diseaseKey].weight < weight) { result[diseaseKey] = { diseaseKey, roleLabel, roleDescription, weight }; } }; // cross_refs 기반 ((drug.row && drug.row.cross_refs) || []).forEach(ref => { if (!ref || ref.major !== 'CV') return; const key = NAME_TO_DISEASE_KEY[ref.disease] || NAME_TO_DISEASE_KEY[ref.drug_class]; if (key) addCandidate(key, ref.drug_class, ref.description || '', 0.8); }); // DRUG_ROLE_MAP 기반 (basket_tokens × roleForToken) (drug.basket_tokens || []).forEach(token => { cvRoleKeys.forEach(key => { const role = roleForToken(key, token, drug.drug_class); if (role) addCandidate(key, role.roleLabel, role.roleDescription, roleWeight(role)); }); }); // 주질환(drug.row.disease) 기반 if (drug.row && drug.row.disease) { const key = NAME_TO_DISEASE_KEY[drug.row.disease]; if (key && !result[key]) addCandidate(key, drug.drug_class || '', '', 0.6); } return Object.values(result); }; // ── 콤보 라벨 DB 조회 헬퍼 ────────────────────────────────────────── // build_cv_combo_matrix.py 와 동일한 규칙: sorted(tokens).join('+') // SUBCLASS_BY_GENERIC 계열은 drug_class::generic 세분화 const COMBO_SUBCLASS_CLASSES = new Set([ 'Antiplatelet (P2Y12)', 'Direct Oral Anticoagulant (DOAC)', 'Prostacyclin analogues', '말초혈액순환 개선', '정맥순환 개선', ]); const makeComboKey = (drugs) => { const tokens = drugs.map(d => { const dc = d.drug_class || ''; if (COMBO_SUBCLASS_CLASSES.has(dc) && d.generic) return `${dc}::${d.generic}`; return dc; }).filter(Boolean); return [...new Set(tokens)].sort().join('+'); }; // 라벨 disease 문자열 → CV_ 키 배열 (greedy seed용) // "고혈압+이상지질혈증" → ["CV_HyperT", "CV_DysL"] // (비CV) 표기는 스킵, diseaseKeys 내에 없는 키도 스킵 const labelDiseaseToKeys = (diseaseStr, validKeys) => { if (!diseaseStr) return []; return diseaseStr.split('+') .map(d => d.replace(/\(비CV\)|\（비CV\）/g, '').trim()) .filter(d => d && !d.includes('비CV')) .map(d => NAME_TO_DISEASE_KEY[d]) .filter(k => k && (!validKeys || validKeys.includes(k))); }; // Stage 2: 최대 부분집합 탐색 const findBestSubset = (comboKey) => { const inputTokens = comboKey.split('+'); let bestLabel = null, bestSize = 0; Object.keys(CV_COMBO_LABELS).forEach(key => { const keyTokens = key.split('+'); if (keyTokens.every(t => inputTokens.includes(t))) { if (keyTokens.length > bestSize) { bestSize = keyTokens.length; bestLabel = CV_COMBO_LABELS[key]; } } }); return bestLabel; }; // B-3. 집합 커버 방식 시나리오 생성 const buildCombinationScenarios = (matchedCvDrugs) => { // Guard clause if (!matchedCvDrugs || matchedCvDrugs.length === 0) return []; // ── Stage 3: 기존 greedy IDF — Stage 1/2 seed 삽입은 아래 tryScenario 호출부에서 ── const drugCandidates = matchedCvDrugs.map(drug => ({ drug, candidates: drugDiseaseCandidates(drug), })); if (drugCandidates.every(d => d.candidates.length === 0)) return []; // 질환별 커버 가능 약물 집합 const diseaseToRoles = {}; drugCandidates.forEach(({ drug, candidates }) => { candidates.forEach(cand => { if (!diseaseToRoles[cand.diseaseKey]) diseaseToRoles[cand.diseaseKey] = []; diseaseToRoles[cand.diseaseKey].push({ drug, ...cand }); }); }); const diseaseKeys = Object.keys(diseaseToRoles); if (!diseaseKeys.length) return []; // 그리디 집합 커버 실행 (시작 질환 강제 가능) const runGreedy = (forceFirst) => { const uncovered = new Set(matchedCvDrugs.map((_, i) => i)); const chosen = []; let remaining = [...diseaseKeys]; if (forceFirst) { remaining = [forceFirst, ...diseaseKeys.filter(k => k !== forceFirst)]; } while (uncovered.size > 0 && remaining.length > 0) { let best = null, bestCount = -1, bestWeight = -1; remaining.forEach(key => { const coverable = (diseaseToRoles[key] || []).filter(r => { const idx = matchedCvDrugs.indexOf(r.drug); return uncovered.has(idx); }); const w = coverable.reduce((s, r) => s + r.weight, 0); if (coverable.length > bestCount || (coverable.length === bestCount && w > bestWeight)) { best = key; bestCount = coverable.length; bestWeight = w; } }); if (!best || bestCount === 0) break; chosen.push(best); remaining = remaining.filter(k => k !== best); (diseaseToRoles[best] || []).forEach(r => { const idx = matchedCvDrugs.indexOf(r.drug); uncovered.delete(idx); }); } return chosen; }; // 점수 계산 헬퍼 const scoreScenario = (diseaseKeyList) => { const assignedDrugs = new Set(); const diseases = diseaseKeyList.map(key => { const label = (DATA && DATA[key] && DATA[key].meta && DATA[key].meta.diseaseLabel) || key; const drugs = (diseaseToRoles[key] || []) .filter(r => !assignedDrugs.has(r.drug)) .map(r => { assignedDrugs.add(r.drug); return r; }) .map(r => ({ brand: r.drug.brand, generic: r.drug.generic, roleLabel: r.roleLabel, roleDescription: r.roleDescription })); return { diseaseKey: key, diseaseLabel: label, drugs }; }); const covered = [...assignedDrugs].length; const total = matchedCvDrugs.length; const coverage = covered / Math.max(total, 1); const allRoles = diseaseKeyList.flatMap(k => diseaseToRoles[k] || []); const avgRoleWeight = allRoles.length ? allRoles.reduce((s, r) => s + r.weight, 0) / allRoles.length : 0; const parsimony = 1 - (diseaseKeyList.length - 1) / Math.max(diseaseKeys.length, 1); const rawScore = coverage * 0.4 + avgRoleWeight * 0.35 + parsimony * 0.25; return { rawScore, diseases }; }; // 후보 시나리오 생성 (기본 + 대안 최대 4개) const candidates = []; const seenSigs = new Set(); const tryScenario = (forceFirst) => { const chosen = runGreedy(forceFirst); if (!chosen.length) return; const sig = chosen.sort().join('|'); if (seenSigs.has(sig)) return; seenSigs.add(sig); const { rawScore, diseases } = scoreScenario(chosen); candidates.push({ rawScore, diseases }); }; // ── Stage 1/2: 라벨 DB 기반 직접 반환 ── let trustLevel = 'low'; if (Object.keys(CV_COMBO_LABELS).length > 0) { const comboKey = makeComboKey(matchedCvDrugs); const exactLabel = CV_COMBO_LABELS[comboKey]; if (exactLabel && exactLabel.scenarios && exactLabel.scenarios.length) { // Stage 1: 정확 매칭 → 라벨 scenarios를 직접 후보로 추가 (greedy 없음) trustLevel = 'high'; exactLabel.scenarios.forEach(sc => { const keys = labelDiseaseToKeys(sc.disease, diseaseKeys); if (!keys.length) return; const sig = [...keys].sort().join('|'); if (seenSigs.has(sig)) return; seenSigs.add(sig); const { rawScore, diseases } = scoreScenario(keys); candidates.push({ rawScore, diseases }); }); } else { const subsetLabel = findBestSubset(comboKey); if (subsetLabel && subsetLabel.scenarios && subsetLabel.scenarios.length) { // Stage 2: 부분집합 매칭 → 1순위 scenario 직접 추가 + greedy 보완 trustLevel = 'medium'; const keys = labelDiseaseToKeys(subsetLabel.scenarios[0].disease, diseaseKeys); if (keys.length) { const sig = [...keys].sort().join('|'); if (!seenSigs.has(sig)) { seenSigs.add(sig); const { rawScore, diseases } = scoreScenario(keys); candidates.push({ rawScore, diseases }); } } tryScenario(keys[0] || null); } } } // 라벨 seed 없거나 추가 후보 탐색 tryScenario(null); diseaseKeys.slice(0, 4).forEach(key => tryScenario(key)); if (!candidates.length) return []; // 절대 점수 기준 내림차순 정렬 (역전 허용) return candidates .map(c => ({ ...c, score: Math.round(c.rawScore * 100), trustLevel })) .sort((a, b) => b.score - a.score) .slice(0, 3); }; const analyzeOcrDrugsForCv = (ocrDrugs) => { const matches = (ocrDrugs || []).map(matchOcrDrugToProducts); const matchedCvDrugs = matches .filter(match => match && match.matched && match.best && match.best.isCv) .map(match => ({ ...match.best, topCandidates: match.candidates, })); const recognizedNonCvDrugs = matches .filter(match => match && match.matched && match.best && !match.best.isCv) .map(match => ({ ...match.best, topCandidates: match.candidates, })); const unmatchedDrugs = (ocrDrugs || []) .map((drug, idx) => ({ drug, match: matches[idx] })) .filter(item => !item.match || !item.match.matched || !item.match.best) .map(item => ({ original_name: ocrTextFromDrug(item.drug), dosage: item.drug && item.drug.dosage, frequency: item.drug && item.drug.frequency, duration: item.drug && item.drug.duration, usage: item.drug && item.drug.usage, candidates: item.match ? item.match.candidates : [], })); const cvBasketTokens = [...new Set(matchedCvDrugs.flatMap(drug => drug.basket_tokens || []))].sort(); const candidateContexts = rankCvCandidateContexts(matchedCvDrugs); const combinationScenarios = buildCombinationScenarios(matchedCvDrugs); const doseCounselingNotes = matchedCvDrugs.flatMap(doseCounselingForDrug); return { rawDrugs: ocrDrugs || [], matchedCvDrugs, recognizedNonCvDrugs, unmatchedDrugs, cvBasketTokens, candidateContexts, combinationScenarios, doseCounselingNotes, }; }; const createCustomPatternFromOcr = (ocrDrugs, existingAnalysis) => { const analysis = existingAnalysis || analyzeOcrDrugsForCv(ocrDrugs); const mappedDrugs = [ ...analysis.matchedCvDrugs, ...analysis.unmatchedDrugs.map(drug => ({ brand: drug.original_name, generic: drug.original_name, drug_class: '분류불명', dose: drug.dosage, dosage: drug.dosage, frequency: drug.frequency, duration: drug.duration, days: drug.duration, usage: drug.usage, timing: drug.usage, })), ]; const topContext = analysis.candidateContexts[0]; const bestPatternIdx = topContext && topContext.bestPattern ? idxOf(topContext.diseaseKey, topContext.bestPattern.patternId) : -1; const bestPattern = bestPatternIdx >= 0 ? getPattern(topContext.diseaseKey, bestPatternIdx) : null; return { ...(bestPattern || {}), patternId: bestPattern ? `OCR-Match-${bestPattern.patternId}` : 'OCR-Result', title: 'OCR 분석 기반 추정 처방전', diseases: topContext ? [topContext.diseaseLabel] : ['미상'], context: topContext ? `[OCR 기반 자동 매칭] 인식된 CV 약물 조합은 ${topContext.diseaseLabel} 맥락이 가장 높게 추정됩니다. 다른 가능성도 함께 확인해야 합니다.` : '처방전에서 인식된 약품 중 CV 표준 처방 조합과 충분히 매칭되는 항목을 찾지 못했습니다.', aiInterpretation: topContext ? `${topContext.diseaseLabel} 가능성이 가장 높지만, 동일 약물이 여러 CV 질환에서 쓰일 수 있어 아래 후보 맥락을 함께 검토해야 합니다.` : '약물명 교정 또는 비CV 약물 여부 확인이 필요합니다.', drugs: mappedDrugs.map((drug, i) => ({ ...drug, groupId: `g${i}` })), drugGroups: mappedDrugs.map((drug, i) => ({ id: `g${i}`, label: drug.drug_class || '분류불명' })), candidate_contexts: analysis.candidateContexts, dose_counseling_notes: analysis.doseCounselingNotes, ocr_analysis: analysis, _diseaseKey: topContext ? topContext.diseaseKey : '__all', _sourceFile: topContext ? `${topContext.diseaseKey}_rx.json` : 'unknown', }; }; return { DATA, ORDER, diseaseList, patternsFor, getPattern, weightedRandom, idxOf, groupOf, fmt, cautionItems, labelOf, scopedPatterns, scopedIdxOf, contextNotesForPattern, differentialDxForPattern, analyzeOcrDrugsForCv, createCustomPatternFromOcr, qualityFeedbackPreflight, sanitizeOcrAnalysisForStorage, fuzzyCandidates, productBrandList }; })(); // ── tiny custom dropdown ───────────────────────────────────── function Dropdown({ value, items, onChange, placeholder, align = 'left', width }) { const [open, setOpen] = React.useState(false); const ref = React.useRef(null); React.useEffect(() => { const h = (e) => { if (ref.current && !ref.current.contains(e.target)) setOpen(false); }; document.addEventListener('mousedown', h); return () => document.removeEventListener('mousedown', h); }, []); const cur = items.find(i => i.value === value); return (
{open && (
{items.map(it => ( ))}
)}
); } // ── selection-basis badge ──────────────────────────────────── function SelectionBadge({ basis, small }) { const clin = basis === 'clinical'; return ( {clin ? '임상적 선택' : '다빈도 선택'} ); } // ── 권한 체크 (단일 진실 공급원) ────────────────────────────── function isUnlocked(plan) { const p = plan || 'free'; return p === 'vip' || p === 'admin' || p === 'trial'; } window.RX = RX; window.rxHelpers = rxHelpers; window.Dropdown = Dropdown; window.SelectionBadge = SelectionBadge; window.isUnlocked = isUnlocked;