284 lines
9.6 KiB
Python
284 lines
9.6 KiB
Python
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"""
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DeepSeek医疗数据分析器
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使用DeepSeek API分析OCR提取的医疗数据，补充缺失的参考范围和单位
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"""
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import json
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import requests
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from typing import List, Dict
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class DeepSeekAnalyzer:
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def __init__(self, api_key: str):
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self.api_key = api_key
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self.api_url = "https://api.deepseek.com/v1/chat/completions"
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self.headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json"
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}
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def analyze_medical_data(self, items: List[Dict]) -> List[Dict]:
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"""
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分析医疗数据，补充缺失的参考范围、单位和提示
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Args:
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items: OCR提取的医疗检测项列表
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Returns:
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补充完整的医疗检测项列表
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"""
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# 分批处理，每批20个项目
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batch_size = 20
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all_results = []
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for i in range(0, len(items), batch_size):
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batch = items[i:i+batch_size]
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print(f" 处理第 {i//batch_size + 1} 批 ({len(batch)} 项)...")
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result = self._analyze_batch(batch)
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if result:
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all_results.extend(result)
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else:
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# 如果API调用失败，保留原始数据
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all_results.extend(batch)
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return all_results
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def _analyze_batch(self, items: List[Dict]) -> List[Dict]:
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"""分析一批医疗数据"""
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# 构建提示词
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prompt = self._build_prompt(items)
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try:
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response = requests.post(
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self.api_url,
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headers=self.headers,
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json={
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"model": "deepseek-chat",
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"messages": [
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{
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"role": "system",
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"content": """你是一个专业的医学检验数据分析专家。你的任务是：
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1. 分析医疗检测项目数据
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2. 为缺失参考范围(reference)的项目补充标准参考范围
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3. 为缺失单位(unit)的项目补充正确单位
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4. 判断结果是否在正常范围内：
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- 如果结果在正常范围内，point字段设为空字符串""
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- 如果结果高于正常范围，point字段设为"↑"
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- 如果结果低于正常范围，point字段设为"↓"
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- 如果是定性结果（如Negative/Positive），且结果正常，point为空；异常则标注
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请严格按照JSON格式返回，不要添加任何额外说明。"""
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},
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{
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"role": "user",
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"content": prompt
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}
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],
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"temperature": 0.1,
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"max_tokens": 4000
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},
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timeout=60
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)
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if response.status_code == 200:
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result = response.json()
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content = result['choices'][0]['message']['content']
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# 解析JSON响应
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# 处理可能的markdown代码块
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if '```json' in content:
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content = content.split('```json')[1].split('```')[0]
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elif '```' in content:
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content = content.split('```')[1].split('```')[0]
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return json.loads(content.strip())
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else:
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print(f" ⚠ API错误: {response.status_code} - {response.text[:100]}")
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return None
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except json.JSONDecodeError as e:
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print(f" ⚠ JSON解析错误: {e}")
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return None
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except requests.exceptions.Timeout:
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print(" ⚠ API请求超时")
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return None
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except Exception as e:
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print(f" ⚠ 请求错误: {e}")
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return None
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def _build_prompt(self, items: List[Dict]) -> str:
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"""构建分析提示词"""
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# 简化数据，只保留必要字段
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simplified = []
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for item in items:
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simplified.append({
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"abb": item.get("abb", ""),
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"project": item.get("project", ""),
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"result": item.get("result", ""),
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"point": item.get("point", ""),
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"unit": item.get("unit", ""),
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"reference": item.get("reference", "")
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})
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prompt = f"""请分析以下医疗检测数据，补充缺失的参考范围和单位，并判断结果是否正常：
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{json.dumps(simplified, ensure_ascii=False, indent=2)}
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要求：
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1. 为每个项目补充完整的reference（参考范围）和unit（单位）
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2. 根据result判断是否在正常范围内，设置point字段（正常为空，偏高为"↑"，偏低为"↓"）
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3. 定性结果（如Negative、Positive）：正常时point为空，异常时根据具体情况标注
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4. 保持原有的abb、project、result字段不变
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请直接返回JSON数组格式，不要添加任何说明文字："""
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return prompt
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def test_deepseek():
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"""测试DeepSeek API"""
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# 需要替换为实际的API Key
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api_key = "YOUR_DEEPSEEK_API_KEY"
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analyzer = DeepSeekAnalyzer(api_key)
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# 测试数据
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test_items = [
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{"abb": "WBC", "project": "White Blood Cell", "result": "5.95", "point": "", "unit": "", "reference": ""},
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{"abb": "PRO", "project": "Protein", "result": "Negative", "point": "", "unit": "", "reference": ""},
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{"abb": "GLU", "project": "Glucose", "result": "6.5", "point": "", "unit": "", "reference": ""},
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]
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result = analyzer.analyze_medical_data(test_items)
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print(json.dumps(result, ensure_ascii=False, indent=2))
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def call_deepseek(prompt: str, api_key: str) -> str:
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"""调用DeepSeek API（通用接口）"""
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url = "https://api.deepseek.com/v1/chat/completions"
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json"
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}
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data = {
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"model": "deepseek-chat",
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"messages": [
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{"role": "user", "content": prompt}
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],
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"temperature": 0.1,
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"max_tokens": 8000
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}
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response = requests.post(url, headers=headers, json=data, timeout=120)
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response.raise_for_status()
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return response.json()["choices"][0]["message"]["content"]
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def process_with_deepseek(ocr_data: list, template_abbs: list, api_key: str) -> dict:
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"""让DeepSeek处理OCR数据并匹配到模板ABB"""
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prompt = f"""你是医疗数据处理专家。请处理以下OCR提取的医疗检测数据，并匹配到模板中的ABB。
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## OCR提取的原始数据：
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```json
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{json.dumps(ocr_data, ensure_ascii=False, indent=2)}
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```
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## 模板中需要填充的ABB列表：
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{template_abbs}
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## 任务要求：
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1. 清理OCR数据中的错误和噪音
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2. 将每个有效数据项匹配到正确的模板ABB
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3. 正确分离result（结果）、unit（单位）、reference（参考范围）
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4. 对于尿检项目（如PRO、GLU、KET、NIT等），结果通常是Negative/Positive，这是正确的定性结果
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5. 过滤掉明显错误的数据（如result为"."、"0"、空值等）
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6. 如果同一个ABB有多条数据，选择最合理的一条
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## 输出格式：
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请返回JSON格式，结构如下：
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```json
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{{
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"ABB1": {{"result": "数值或定性结果", "unit": "单位", "reference": "参考范围", "point": "提示"}},
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"ABB2": {{"result": "...", "unit": "...", "reference": "...", "point": ""}},
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...
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}}
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```
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只返回JSON，不要其他说明文字。确保JSON格式正确可解析。
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"""
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print("正在调用DeepSeek处理数据...")
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result = call_deepseek(prompt, api_key)
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# 提取JSON
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try:
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# 尝试直接解析
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return json.loads(result)
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except:
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# 尝试从markdown代码块提取
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import re
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json_match = re.search(r'```(?:json)?\s*([\s\S]*?)\s*```', result)
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if json_match:
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return json.loads(json_match.group(1))
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raise ValueError(f"无法解析DeepSeek返回的JSON: {result[:500]}")
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def process_ocr_data_main():
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"""
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处理OCR数据的主函数（原deepseek_process.py的main函数）
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"""
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from pathlib import Path
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import os
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# 从环境变量获取API Key
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api_key = os.environ.get("DEEPSEEK_API_KEY", "")
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if not api_key:
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api_key = input("请输入DeepSeek API Key: ").strip()
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if not api_key:
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print("❌ API Key不能为空")
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return
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# 加载OCR数据
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ocr_file = Path(__file__).parent / "extracted_medical_data.json"
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if not ocr_file.exists():
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print("❌ 未找到OCR数据文件")
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return
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with open(ocr_file, 'r', encoding='utf-8') as f:
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data = json.load(f)
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ocr_items = data.get('items', data) if isinstance(data, dict) else data
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print(f"加载 {len(ocr_items)} 条OCR数据")
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# 加载模板ABB配置
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from config import load_abb_config
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config = load_abb_config()
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template_abbs = config.get('abb_list', [])
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print(f"模板中有 {len(template_abbs)} 个ABB")
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# 调用DeepSeek处理
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processed_data = process_with_deepseek(ocr_items, template_abbs, api_key)
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# 保存处理后的数据
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output_file = Path(__file__).parent / "deepseek_processed_data.json"
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with open(output_file, 'w', encoding='utf-8') as f:
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json.dump(processed_data, f, ensure_ascii=False, indent=2)
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print(f"✅ DeepSeek处理完成，共 {len(processed_data)} 个有效项")
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print(f"✅ 已保存到: {output_file}")
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return processed_data
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if __name__ == "__main__":
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import sys
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if len(sys.argv) > 1 and sys.argv[1] == '--process':
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process_ocr_data_main()
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else:
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test_deepseek()
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