refactor(asl): ASL frontend architecture refactoring with left navigation
- feat: Create ASLLayout component with 7-module left navigation - feat: Implement Title Screening Settings page with optimized PICOS layout - feat: Add placeholder pages for Workbench and Results - fix: Fix nested routing structure for React Router v6 - fix: Resolve Spin component warning in MainLayout - fix: Add QueryClientProvider to App.tsx - style: Optimize PICOS form layout (P+I left, C+O+S right) - style: Align Inclusion/Exclusion criteria side-by-side - docs: Add architecture refactoring and routing fix reports Ref: Week 2 Frontend Development Scope: ASL module MVP - Title Abstract Screening
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backend/scripts/test-stroke-screening.ts
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293
backend/scripts/test-stroke-screening.ts
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/**
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* 卒中文献筛选测试脚本
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* 用真实数据验证泛化能力
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*/
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import XLSX from 'xlsx';
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import * as path from 'path';
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import { fileURLToPath } from 'url';
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import { llmScreeningService } from '../src/modules/asl/services/llmScreeningService.js';
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const __filename = fileURLToPath(import.meta.url);
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const __dirname = path.dirname(__filename);
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// 卒中研究的PICOS（从测试文档读取）
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const STROKE_PICOS = {
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population: "非心源性缺血性卒中（NCIS）患者、亚洲人群",
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intervention: "抗血小板治疗药物（阿司匹林、氯吡格雷、奥扎格雷、贝前列素、西洛他唑、替罗非班、替格瑞洛、吲哚布芬、沙格雷酯、氯吡格雷阿司匹林、双嘧达莫等）或抗凝药物（阿加曲班、asundexian、milvexian、华法林、低分子肝素、肝素等）或溶栓药物（链激酶、尿激酶、阿替普酶、替奈普酶等）",
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comparison: "安慰剂或常规治疗",
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outcome: "疗效安全性：卒中进展、神经功能恶化、卒中复发、残疾、死亡、NIHSS评分变化、VTE、痴呆、认知功能减退、疲乏、抑郁等",
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studyDesign: "系统评价（SR）、随机对照试验（RCT）、真实世界研究（RWE）、观察性研究（OBS）"
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};
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// 纳入标准
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const INCLUSION_CRITERIA = `
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1. 非心源性缺血性卒中、亚洲患者
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2. 卒中后接受二级预防治疗的患者（Secondary Stroke Prevention, SSP）
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3. 干预措施为抗血小板、抗凝或溶栓药物
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4. 报告疗效或安全性结局（卒中进展、复发、残疾、死亡等）
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5. 研究类型：系统评价、RCT、真实世界研究、观察性研究
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6. 研究时间：2020年之后的文献
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7. 包含"二级预防"或"预防复发"或"卒中预防"相关内容
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8. 涉及抗血小板或抗凝药物
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`;
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// 排除标准
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const EXCLUSION_CRITERIA = `
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1. 心源性卒中患者、非亚洲人群
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2. 其他类型卒中（非缺血性）
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3. 用于急性冠脉综合征（ACS）的抗血小板治疗，未明确提及卒中
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4. 房颤（AF）患者
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5. 混合人群（包含非卒中患者）
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6. 病例报告
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7. 非中英文文献
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8. 仅包含急性期治疗（如急性期溶栓、取栓），未涉及二级预防
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`;
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interface TestCase {
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index: number;
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pmid: string;
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title: string;
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abstract: string;
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humanDecision: string; // Include/Exclude
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excludeReason?: string;
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}
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async function readExcelTestCases(filePath: string, limit: number = 5): Promise<TestCase[]> {
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console.log(`📖 读取Excel文件: ${filePath}`);
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const workbook = XLSX.readFile(filePath);
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const sheetName = workbook.SheetNames[0];
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const worksheet = workbook.Sheets[sheetName];
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const data = XLSX.utils.sheet_to_json(worksheet);
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console.log(`✅ 读取到 ${data.length} 条数据`);
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// 分别提取Included和Excluded的案例（混合测试）
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const includedCases: any[] = [];
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const excludedCases: any[] = [];
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for (const row of data as any[]) {
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// 跳过没有标题或摘要的行
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if (!row['title'] || !row['abstract']) {
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continue;
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}
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if (row['Decision'] && row['Decision'].toLowerCase().includes('include')) {
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includedCases.push(row);
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} else if (row['Decision'] && row['Decision'].toLowerCase().includes('exclude')) {
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excludedCases.push(row);
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}
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}
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console.log(` - Included案例: ${includedCases.length}条`);
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console.log(` - Excluded案例: ${excludedCases.length}条`);
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// 混合选择：2个Included + 3个Excluded
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const testCases: TestCase[] = [];
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// 取前2个Included
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for (let i = 0; i < Math.min(2, includedCases.length); i++) {
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const row = includedCases[i];
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testCases.push({
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index: testCases.length + 1,
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pmid: row['key'] || `test-${testCases.length + 1}`,
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title: row['title'] || '',
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abstract: row['abstract'] || '',
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humanDecision: row['Decision'] || 'Unknown',
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excludeReason: row['Reason for excluded'] || undefined
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});
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}
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// 取前3个Excluded
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for (let i = 0; i < Math.min(3, excludedCases.length); i++) {
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const row = excludedCases[i];
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testCases.push({
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index: testCases.length + 1,
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pmid: row['key'] || `test-${testCases.length + 1}`,
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title: row['title'] || '',
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abstract: row['abstract'] || '',
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humanDecision: row['Decision'] || 'Unknown',
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excludeReason: row['Reason for excluded'] || undefined
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});
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}
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console.log(`✅ 提取 ${testCases.length} 条有效测试案例 (${testCases.filter(t => t.humanDecision.toLowerCase().includes('include')).length} Included + ${testCases.filter(t => t.humanDecision.toLowerCase().includes('exclude')).length} Excluded)\n`);
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return testCases;
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}
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async function testSingleLiterature(
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testCase: TestCase,
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models: [string, string]
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): Promise<{
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testCase: TestCase;
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aiDecision: string;
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isCorrect: boolean;
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hasConsensus: boolean;
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details: any;
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}> {
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console.log(`\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━`);
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console.log(`[${testCase.index}] PMID: ${testCase.pmid}`);
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console.log(`标题: ${testCase.title.substring(0, 100)}...`);
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console.log(`人类判断: ${testCase.humanDecision}`);
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try {
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const startTime = Date.now();
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const result = await llmScreeningService.dualModelScreening(
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testCase.pmid || `test-${testCase.index}`,
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testCase.title,
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testCase.abstract,
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STROKE_PICOS,
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INCLUSION_CRITERIA,
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EXCLUSION_CRITERIA,
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models
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);
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const duration = Date.now() - startTime;
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// 映射AI决策到Include/Exclude
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let aiDecision = 'Unknown';
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if (result.finalDecision === 'include') {
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aiDecision = 'Include';
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} else if (result.finalDecision === 'exclude') {
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aiDecision = 'Exclude';
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} else {
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aiDecision = 'Uncertain';
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}
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// 标准化比较（处理Included/Include, Excluded/Exclude的差异）
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const normalizeDecision = (decision: string) => {
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const lower = decision.toLowerCase();
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if (lower.includes('include')) return 'include';
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if (lower.includes('exclude')) return 'exclude';
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return lower;
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};
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const isCorrect = normalizeDecision(aiDecision) === normalizeDecision(testCase.humanDecision);
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console.log(`AI判断: ${aiDecision}`);
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console.log(`DeepSeek: ${result.deepseek.conclusion} (置信度: ${result.deepseek.confidence})`);
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console.log(`Qwen: ${result.qwen.conclusion} (置信度: ${result.qwen.confidence})`);
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console.log(`一致性: ${result.hasConflict ? '❌ 冲突' : '✅ 一致'}`);
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console.log(`结果: ${isCorrect ? '✅ 正确' : '❌ 错误'}`);
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console.log(`耗时: ${duration}ms`);
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if (!isCorrect) {
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console.log(`\n❌ 判断错误！`);
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console.log(`期望: ${testCase.humanDecision}`);
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console.log(`实际: ${aiDecision}`);
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if (testCase.excludeReason) {
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console.log(`人类排除理由: ${testCase.excludeReason}`);
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}
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console.log(`DeepSeek理由: ${result.deepseek.reason}`);
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console.log(`Qwen理由: ${result.qwen.reason}`);
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}
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return {
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testCase,
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aiDecision,
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isCorrect,
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hasConsensus: !result.hasConflict,
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details: result
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};
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} catch (error) {
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console.error(`❌ 测试失败:`, error);
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return {
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testCase,
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aiDecision: 'Error',
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isCorrect: false,
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hasConsensus: false,
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details: null
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};
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}
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}
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async function main() {
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console.log('\n🔬 卒中文献筛选测试');
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console.log('=' .repeat(60));
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console.log('目的: 验证系统对不同研究主题的泛化能力\n');
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// 读取测试数据
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const excelPath = path.join(__dirname, '../docs/03-业务模块/ASL-AI智能文献/05-测试文档/03-测试数据/screening/Test Cases.xlsx');
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let testCases: TestCase[];
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try {
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testCases = await readExcelTestCases(excelPath, 5);
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} catch (error: any) {
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console.error('❌ 读取Excel失败，尝试使用绝对路径...');
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const absolutePath = 'D:\\MyCursor\\AIclinicalresearch\\docs\\03-业务模块\\ASL-AI智能文献\\05-测试文档\\03-测试数据\\screening\\Test Cases.xlsx';
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testCases = await readExcelTestCases(absolutePath, 5);
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}
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if (testCases.length === 0) {
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console.error('❌ 没有读取到有效的测试案例');
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return;
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}
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console.log('📋 PICOS标准:');
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console.log(`P: ${STROKE_PICOS.population}`);
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console.log(`I: ${STROKE_PICOS.intervention.substring(0, 80)}...`);
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console.log(`C: ${STROKE_PICOS.comparison}`);
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console.log(`O: ${STROKE_PICOS.outcome.substring(0, 80)}...`);
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console.log(`S: ${STROKE_PICOS.studyDesign}`);
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console.log('\n🚀 开始测试...');
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console.log(`测试样本数: ${testCases.length}`);
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console.log(`测试模型: DeepSeek-V3 + Qwen-Max\n`);
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const results: any[] = [];
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for (const testCase of testCases) {
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const result = await testSingleLiterature(testCase, ['deepseek-chat', 'qwen-max']);
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results.push(result);
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// 避免API限流
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if (testCases.indexOf(testCase) < testCases.length - 1) {
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await new Promise(resolve => setTimeout(resolve, 2000));
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}
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}
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// 统计结果
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console.log('\n\n' + '='.repeat(60));
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console.log('📊 测试结果统计');
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console.log('='.repeat(60));
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const totalTests = results.length;
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const correctCount = results.filter(r => r.isCorrect).length;
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const consensusCount = results.filter(r => r.hasConsensus).length;
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const accuracy = totalTests > 0 ? (correctCount / totalTests * 100).toFixed(1) : '0.0';
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const consensusRate = totalTests > 0 ? (consensusCount / totalTests * 100).toFixed(1) : '0.0';
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console.log(`\n总测试数: ${totalTests}`);
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console.log(`正确判断: ${correctCount}`);
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console.log(`准确率: ${accuracy}% ${parseFloat(accuracy) >= 85 ? '✅' : '❌'} (目标≥85%)`);
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console.log(`双模型一致率: ${consensusRate}% ${parseFloat(consensusRate) >= 80 ? '✅' : '❌'} (目标≥80%)`);
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console.log('\n📋 详细结果:');
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results.forEach((r, i) => {
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console.log(`${i + 1}. ${r.isCorrect ? '✅' : '❌'} PMID:${r.testCase.pmid} - 期望:${r.testCase.humanDecision}, AI:${r.aiDecision}`);
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});
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// 结论
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console.log('\n' + '='.repeat(60));
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console.log('🎯 结论');
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console.log('='.repeat(60));
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if (parseFloat(accuracy) >= 85) {
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console.log('✅ 测试通过！系统对卒中研究的筛选准确率达标！');
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console.log('📝 建议: 可以继续开发PICOS配置界面，实现MVP。');
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} else if (parseFloat(accuracy) >= 60) {
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console.log('⚠️ 准确率中等。系统有一定泛化能力，但需要优化。');
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console.log('📝 建议: 分析错误案例，优化Prompt模板。');
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} else {
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console.log('❌ 准确率较低。当前Prompt对卒中研究泛化能力不足。');
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console.log('📝 建议: 需要重新设计Prompt策略，或考虑用户自定义方案。');
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}
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console.log('='.repeat(60) + '\n');
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}
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main().catch(console.error);
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