iomgaa
7c723fbc4b
删除了不再使用的disease_analyst模块的所有相关文件： - agent.py: 疾病分析智能体主逻辑 - prompt.py: 疾病分析提示模板 - response_model.py: 响应数据模型 - __init__.py: 模块初始化文件 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
296 lines
9.6 KiB
Python
Executable File
296 lines
9.6 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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病例数据提取器
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用于提取每个病例的原始case_data、完整对话记录和最终生成的医疗信息
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"""
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import json
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import os
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import re
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from pathlib import Path
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from typing import Dict, List, Any, Optional
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def extract_case_data(workflow_file: Path) -> Dict[str, Any]:
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"""
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从工作流文件中提取病例原始数据
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Args:
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workflow_file: 工作流文件路径
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Returns:
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病例原始数据
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"""
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try:
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with open(workflow_file, 'r', encoding='utf-8') as f:
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lines = f.readlines()
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# 提取第一行的workflow_start事件
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if lines:
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first_line = lines[0].strip()
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try:
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first_step = json.loads(first_line)
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if first_step.get('event_type') == 'workflow_start':
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case_data = first_step.get('case_data', {})
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return case_data
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except json.JSONDecodeError:
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return {}
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except Exception as e:
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print(f"读取文件 {workflow_file} 时出错: {e}")
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return {}
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def extract_conversation_history(workflow_file: Path) -> str:
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"""
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提取完整的对话记录
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Args:
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workflow_file: 工作流文件路径
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Returns:
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完整对话记录字符串
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"""
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try:
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with open(workflow_file, 'r', encoding='utf-8') as f:
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lines = f.readlines()
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conversation_parts = []
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for line in lines:
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try:
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step = json.loads(line.strip())
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# 提取患者回应
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if step.get('event_type') == 'patient_response':
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patient_response = step.get('message', '')
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if patient_response:
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conversation_parts.append(f"患者: {patient_response}")
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# 提取医生问题
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if step.get('event_type') == 'agent_execution':
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agent_name = step.get('agent_name', '')
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if agent_name in ['inquirer', 'prompter']:
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output_data = step.get('output_data', {})
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doctor_question = output_data.get('doctor_question', '')
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if doctor_question:
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conversation_parts.append(f"医生: {doctor_question}")
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except json.JSONDecodeError:
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continue
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return '\n'.join(conversation_parts)
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except Exception as e:
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print(f"提取对话历史时出错: {e}")
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return ""
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def extract_final_medical_info(workflow_file: Path) -> Dict[str, str]:
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"""
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提取最终生成的医疗信息（主诉、现病史、既往史）
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Args:
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workflow_file: 工作流文件路径
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Returns:
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包含主诉、现病史、既往史的字典
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"""
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try:
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with open(workflow_file, 'r', encoding='utf-8') as f:
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lines = f.readlines()
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if not lines:
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return {"chief_complaint": "", "hpi": "", "ph": ""}
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# 查找包含最终医疗信息的step_end事件
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chief_complaint = ""
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hpi = ""
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ph = ""
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for line in reversed(lines):
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try:
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step = json.loads(line.strip())
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if step.get('event_type') == 'step_end':
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step_result = step.get('step_result', {})
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chief_complaint = step_result.get('updated_chief_complaint', chief_complaint)
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hpi = step_result.get('updated_hpi', hpi)
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ph = step_result.get('updated_ph', ph)
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# 如果三个都找到了，就返回
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if chief_complaint and hpi and ph:
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break
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except json.JSONDecodeError:
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continue
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return {
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"chief_complaint": chief_complaint,
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"hpi": hpi,
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"ph": ph
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}
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except Exception as e:
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print(f"提取最终医疗信息时出错: {e}")
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return {"chief_complaint": "", "hpi": "", "ph": ""}
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def extract_case_summary(workflow_file: Path) -> Dict[str, Any]:
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"""
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提取完整的病例摘要
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Args:
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workflow_file: 工作流文件路径
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Returns:
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包含所有提取信息的完整摘要
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"""
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case_data = extract_case_data(workflow_file)
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conversation = extract_conversation_history(workflow_file)
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final_info = extract_final_medical_info(workflow_file)
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return {
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"case_id": workflow_file.stem,
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"case_data": case_data,
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"conversation_history": conversation,
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"final_medical_info": final_info,
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"metadata": {
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"total_turns": len(conversation.split('\n')) if conversation else 0,
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"file_path": str(workflow_file),
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"has_case_data": bool(case_data),
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"has_conversation": bool(conversation),
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"has_final_info": any(final_info.values())
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}
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}
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def process_all_cases(data_dir: str, output_dir: str) -> None:
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"""
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处理所有病例文件
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Args:
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data_dir: 工作流数据目录
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output_dir: 输出目录
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"""
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data_path = Path(data_dir)
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output_path = Path(output_dir)
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output_path.mkdir(parents=True, exist_ok=True)
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workflow_files = list(data_path.glob("workflow_*.jsonl"))
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if not workflow_files:
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print(f"在 {data_dir} 中未找到工作流文件")
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return
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all_cases = []
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failed_cases = []
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for workflow_file in workflow_files:
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try:
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case_summary = extract_case_summary(workflow_file)
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all_cases.append(case_summary)
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# 为每个病例创建单独的文件
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case_output_file = output_path / f"{workflow_file.stem}_summary.json"
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with open(case_output_file, 'w', encoding='utf-8') as f:
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json.dump(case_summary, f, ensure_ascii=False, indent=2)
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except Exception as e:
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print(f"处理文件 {workflow_file} 失败: {e}")
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failed_cases.append(str(workflow_file))
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# 创建汇总文件
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summary = {
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"total_cases": len(all_cases),
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"failed_cases": len(failed_cases),
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"failed_files": failed_cases,
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"cases": all_cases
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}
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with open(output_path / "all_cases_summary.json", 'w', encoding='utf-8') as f:
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json.dump(summary, f, ensure_ascii=False, indent=2)
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# 创建简化汇总（只包含关键信息）
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simple_summary = []
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for case in all_cases:
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simple_case = {
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"case_id": case["case_id"],
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"case_info": {
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"patient_name": case["case_data"].get("病案介绍", {}).get("基本信息", ""),
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"chief_complaint": case["case_data"].get("病案介绍", {}).get("主诉", ""),
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"diagnosis": case["case_data"].get("病案介绍", {}).get("诊断", "")
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},
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"final_output": case["final_medical_info"],
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"conversation_length": len(case["conversation_history"].split('\n')),
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"total_turns": case["metadata"]["total_turns"] // 2 # 医生+患者算一轮
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}
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simple_summary.append(simple_case)
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with open(output_path / "simple_summary.json", 'w', encoding='utf-8') as f:
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json.dump(simple_summary, f, ensure_ascii=False, indent=2)
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print(f"处理完成！")
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print(f"成功处理: {len(all_cases)} 个病例")
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print(f"失败: {len(failed_cases)} 个病例")
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print(f"输出目录: {output_path}")
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def print_case_sample(case_summary: Dict[str, Any], max_conversation_lines: int = 10) -> None:
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"""
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打印病例样本信息
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Args:
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case_summary: 病例摘要
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max_conversation_lines: 最大对话行数
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"""
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print(f"\n=== 病例 {case_summary['case_id']} ===")
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# 病例基本信息
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case_data = case_summary['case_data']
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if case_data and '病案介绍' in case_data:
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case_info = case_data['病案介绍']
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print(f"患者: {case_info.get('基本信息', '未知')}")
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print(f"主诉: {case_info.get('主诉', '未提供')}")
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print(f"诊断: {case_info.get('诊断', '未提供')}")
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# 生成的医疗信息
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final_info = case_summary['final_medical_info']
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print("\n最终生成信息:")
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print(f"主诉: {final_info.get('chief_complaint', '')}")
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print(f"现病史: {final_info.get('hpi', '')[:100]}...")
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print(f"既往史: {final_info.get('ph', '')[:100]}...")
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# 对话摘要
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conversation = case_summary['conversation_history']
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lines = conversation.split('\n')
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print(f"\n对话记录 (共{len(lines)}行):")
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for line in lines[:max_conversation_lines]:
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print(f" {line}")
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if len(lines) > max_conversation_lines:
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print(" ...")
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def main():
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"""主函数"""
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base_dir = Path(__file__).parent.parent
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data_dir = base_dir / "results" / "results0902"
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output_dir = base_dir / "analysis" / "case_extract_0902"
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if not data_dir.exists():
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print(f"数据目录不存在: {data_dir}")
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return
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print("开始提取病例数据...")
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process_all_cases(str(data_dir), str(output_dir))
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# 显示第一个病例作为示例
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output_path = Path(output_dir)
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summary_files = list(output_path.glob("*_summary.json"))
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if summary_files:
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with open(summary_files[0], 'r', encoding='utf-8') as f:
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sample_case = json.load(f)
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print_case_sample(sample_case)
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print("\n提取完成！")
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if __name__ == "__main__":
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main() |