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Family health management including member records, medical history tracking, genetic risk assessment, and health reports
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Based on SOC occupation classification
AI-driven health analysis system including comprehensive analysis, risk prediction, intelligent Q&A, and report generation.
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| name | family |
| description | Family health management including member records, medical history tracking, genetic risk assessment, and health reports |
| argument-hint | <操作类型，如：add-member 父亲 张三 1960-05-15/add-history 父亲 高血压/risk/report> |
| allowed-tools | Read, Write |
| schema | family/schema.json |
Comprehensive family health record management, helping track family medical history, assess genetic risks, and maintain family health.
用户输入 -> 识别操作类型 -> 解析信息 -> 检查完整性 -> [询问补充] -> 生成JSON -> 保存数据
| 输入关键词 | 操作类型 |
|---|---|
| add-member, 添加成员 | 添加家庭成员 |
| add-history, 记录病史, 记录家族史 | 记录家族病史 |
| track, 追踪 | 追踪成员健康 |
| list, 列表 | 列出家庭成员 |
| risk, 风险 | 遗传风险评估 |
| report, 报告 | 生成家庭健康报告 |
| Relationship | Code |
|---|---|
| Self | self |
| Father | father |
| Mother | mother |
| Spouse | spouse |
| Son | son |
| Daughter | daughter |
| Brother | brother |
| Sister | sister |
| Paternal grandfather | paternal_grandfather |
| Paternal grandmother | paternal_grandmother |
| Maternal grandfather | maternal_grandfather |
| Maternal grandmother | maternal_grandmother |
| Category | Disease Keywords |
|---|---|
| Cardiovascular | Hypertension, coronary heart disease, cardiomyopathy, stroke |
| Metabolic | Diabetes, hyperlipidemia, gout, metabolic syndrome |
| Cancer | Lung cancer, breast cancer, colorectal cancer, gastric cancer, liver cancer |
| Respiratory | Asthma, COPD, pulmonary fibrosis |
| Other | Glaucoma, mental illness, autoimmune diseases |
Please provide the following information:
• Member relationship (father/mother/spouse/child, etc.)
• Member name
• Birth date (format: YYYY-MM-DD)
• Blood type (optional)
Please provide the following information:
• When was this member diagnosed? (age or year)
• Current condition? (stable/controlled/needs treatment)
## 步骤 4: 生成 JSON
### 成员数据结构
```json
{
"member_id": "mem_20250108_001",
"name": "张三",
"relationship": "father",
"gender": "male",
"birth_date": "1960-05-15",
"blood_type": "A",
"status": "living",
"created_at": "2025-01-08T10:00:00.000Z"
}
{
"history_id": "hist_20250108_001",
"disease_name": "高血压",
"disease_category": "cardiovascular",
"affected_member_id": "mem_20250108_001",
"age_at_onset": 50,
"severity": "moderate",
"notes": "药物控制良好",
"reported_date": "2025-01-08"
}
{
"disease": "高血压",
"risk_level": "high",
"confidence": "medium",
"affected_members": ["父亲"],
"risk_factors": ["一级亲属患病", "早发(<50岁)"],
"recommendations": [
{
"category": "screening",
"action": "定期血压监测",
"frequency": "每周3次",
"start_age": 35,
"priority": "high"
}
]
}
完整 Schema 定义参见 schema.json。
data/family-health-tracker.json遗传风险评分 = (一级亲属患病数 × 0.4) +
(早发病例数 × 0.3) +
(家族聚集度 × 0.3)
风险等级:
- 高风险: >= 70%
- 中风险: 40%-69%
- 低风险: < 40%
1. 识别操作类型
2. 解析输入参数，提取所有可用信息
3. 检查必填字段，如有缺失则询问用户
4. 计算遗传风险
5. 生成符合 schema.json 的 JSON
6. 保存到数据文件
7. 向用户确认保存成功，并显示摘要和建议
用户: 添加家庭成员 父亲 张三 1960-05-15 A型血
AI: ✅ 家庭成员已添加
成员信息:
关系: 父亲
姓名: 张三
出生日期: 1960年5月15日
血型: A型
年龄: 65岁
已保存至家庭健康档案
提示: 您可以继续添加其他成员或记录家族病史
用户: 记录家族病史 父亲 高血压 50岁确诊
AI: ✅ 家族病史已记录
病史信息:
成员: 父亲 (张三)
疾病: 高血压
发病年龄: 50岁
疾病类别: 心血管疾病
风险评估: 高风险
(一级亲属患病 + 早发病例)
预防建议:
• 35岁开始定期血压监测
• 每周测量3次
• 限制钠盐摄入
• 规律有氧运动
• 控制体重
已保存至家族病史记录
用户: 遗传风险 高血压
AI: 📊 高血压遗传风险评估
风险等级: 高 ⚠️
受影响家庭成员:
• 父亲: 50岁确诊
风险因素:
✓ 一级亲属患病
✓ 早发病例 (<50岁)
风险评分: 75/100
预防建议:
1. 定期血压监测 (35岁开始，每周3次)
2. 限制钠盐摄入 (<5g/天)
3. 规律有氧运动 (每周150分钟)
4. 体重管理 (BMI < 24)
5. 戒烟限酒
筛查建议:
• 每年健康体检
• 监测血压、血脂、血糖
[生成完整报告...]