Files
AIclinicalresearch/docs/02-通用能力层/05-医学NLP引擎/README.md
HaHafeng beb7f7f559 feat(asl): Implement full-text screening core LLM service and validation system (Day 1-3)
Core Components:
- PDFStorageService with Dify/OSS adapters
- LLM12FieldsService with Nougat-first + dual-model + 3-layer JSON parsing
- PromptBuilder for dynamic prompt assembly
- MedicalLogicValidator with 5 rules + fault tolerance
- EvidenceChainValidator for citation integrity
- ConflictDetectionService for dual-model comparison

Prompt Engineering:
- System Prompt (6601 chars, Section-Aware strategy)
- User Prompt template (PICOS context injection)
- JSON Schema (12 fields constraints)
- Cochrane standards (not loaded in MVP)

Key Innovations:
- 3-layer JSON parsing (JSON.parse + json-repair + code block extraction)
- Promise.allSettled for dual-model fault tolerance
- safeGetFieldValue for robust field extraction
- Mixed CN/EN token calculation

Integration Tests:
- integration-test.ts (full test)
- quick-test.ts (quick test)
- cached-result-test.ts (fault tolerance test)

Documentation Updates:
- Development record (Day 2-3 summary)
- Quality assurance strategy (full-text screening)
- Development plan (progress update)
- Module status (v1.1 update)
- Technical debt (10 new items)

Test Results:
- JSON parsing success rate: 100%
- Medical logic validation: 5/5 passed
- Dual-model parallel processing: OK
- Cost per PDF: CNY 0.10

Files: 238 changed, 14383 insertions(+), 32 deletions(-)
Docs: docs/03-涓氬姟妯″潡/ASL-AI鏅鸿兘鏂囩尞/05-寮€鍙戣褰?2025-11-22_Day2-Day3_LLM鏈嶅姟涓庨獙璇佺郴缁熷紑鍙?md
2025-11-22 22:21:12 +08:00

93 lines
1.1 KiB
Markdown
Raw Blame History

This file contains ambiguous Unicode characters
This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.
# 医学NLP引擎
> **能力定位:** 通用能力层
> **复用率:** 14% (1个模块依赖)
> **优先级:** P2
> **状态:** ⏳ 待实现
---
## 📋 能力概述
医学NLP引擎负责
- 医学实体识别NER
- 医学术语标准化
- 疾病/药物识别
---
## 📊 依赖模块
**1个模块依赖14%复用率):**
1. **DC** - 数据清洗整理病例数据NER提取
---
## 💡 核心功能
### 1. 医学实体识别
- 疾病识别
- 药物识别
- 手术识别
- TNM分期提取
### 2. 术语标准化
- ICD编码
- ATC编码
### 3. 关系抽取
- 疾病-药物关系
- 症状-疾病关系
---
## 🏗️ 技术方案
### 云端版(高准确率)
```python
# 基于LLM APIClaude/GPT
# JSON Mode结构化输出
```
### 单机版(隐私优先)
```python
# 基于spaCy + 医学模型
# 100%本地运行
```
---
## 🔗 相关文档
- [通用能力层总览](../README.md)
- [DC模块需求](../../03-业务模块/DC-数据清洗整理/README.md)
---
**最后更新:** 2025-11-06
**维护人:** 技术架构师