医疗AI智能体：合规可追溯：SKILL架构下的可解释性AI（XAI）医疗落地全解.145

class MedicalSkill:
    def __init__(self, skill_id, name, evidence, logic):
        self.skill_id = skill_id
        self.name = name
        self.evidence = evidence  # 医学依据
        self.logic = logic        # 推理规则
    
    def execute(self, data):
        # 执行技能并返回可解释结果
        result, reason, conf = self.logic(data)
        return {
            "skill_id": self.skill_id,
            "skill_name": self.name,
            "evidence": self.evidence,
            "result": result,
            "reason_chain": reason,
            "confidence": conf
        }

# 定义糖尿病风险评估 SKILL
def diabetes_risk_logic(data):
    fbs = data.get("fbs", 0)        # 空腹血糖
    hba1c = data.get("hba1c", 0)    # 糖化
    age = data.get("age", 0)
    
    reason = []
    score = 0.5
    
    if fbs >= 7.0:
        reason.append(f"空腹血糖 {fbs} ≥ 7.0 mmol/L，达到诊断阈值")
        score += 0.2
    if hba1c >= 6.5:
        reason.append(f"糖化血红蛋白 {hba1c} ≥ 6.5%，达到诊断阈值")
        score += 0.2
    if age >= 60:
        reason.append(f"年龄 {age} ≥ 60 岁，属于高危人群")
        score += 0.1
    
    score -= 0.09  # 无既往病史修正
    score = round(score, 2)
    
    if score >= 0.8:
        result = "高度提示2型糖尿病可能"
    elif score >= 0.6:
        result = "糖尿病前期风险"
    else:
        result = "血糖异常风险较低"
    
    return result, reason, score

# 实例化 SKILL
skill_diabetes = MedicalSkill(
    skill_id="SKILL_DIABETES_RISK_v1.2",
    name="糖尿病风险评估技能",
    evidence="《中国2型糖尿病防治指南(2022年版)》",
    logic=diabetes_risk_logic
)

# 模拟患者数据
patient = {
    "age": 62,
    "fbs": 8.5,
    "hba1c": 7.6
}

# 执行并输出可解释结果
output = skill_diabetes.execute(patient)
for k, v in output.items():
    print(f"{k}: {v}")

def generate_xai_report(output):
    print("=" * 50)
    print("【医疗AI可解释诊断报告】")
    print("=" * 50)
    print(f"结论: {output['result']}")
    print(f"触发技能: {output['skill_name']}")
    print(f"医学依据: {output['evidence']}")
    print(f"置信度: {output['confidence']}")
    print("\n判断依据链：")
    for idx, r in enumerate(output['reason_chain'], 1):
        print(f"{idx}. {r}")

generate_xai_report(output)

# main.py (后端)
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import Dict, Any, List
from fastapi.middleware.cors import CORSMiddleware
import uvicorn

app = FastAPI(title="SKILL-XAI 医疗可解释引擎", version="1.0")

# CORS 中间件
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# 医学依据库
EVIDENCE_DB = {
    "diabetes": "《中国2型糖尿病防治指南(2022年版)》"
}

# SKILL 基础类
class MedicalSkill:
    def __init__(self, skill_id: str, name: str, evidence: str, logic_func):
        self.skill_id = skill_id
        self.name = name
        self.evidence = evidence
        self.logic_func = logic_func

    def execute(self, data: Dict[str, Any]) -> Dict[str, Any]:
        res, reason, conf = self.logic_func(data)
        return {
            "skill_id": self.skill_id,
            "skill_name": self.name,
            "evidence": self.evidence,
            "result": res,
            "reason_chain": reason,
            "confidence": round(conf, 2)
        }

# 糖尿病风险 SKILL 逻辑
def diabetes_logic(data):
    fbs = data.get("fbs", 0)
    hba1c = data.get("hba1c", 0)
    age = data.get("age", 0)
    reason = []
    score = 0.5

    if fbs >= 7.0:
        reason.append(f"空腹血糖 {fbs} ≥ 7.0 mmol/L（诊断阈值）")
        score += 0.2
    if hba1c >= 6.5:
        reason.append(f"糖化血红蛋白 {hba1c} ≥ 6.5%（诊断阈值）")
        score += 0.2
    if age >= 60:
        reason.append(f"年龄 {age} ≥ 60 岁（高危人群）")
        score += 0.1

    score -= 0.09
    if score >= 0.8:
        result = "高度提示2型糖尿病"
    elif score >= 0.6:
        result = "糖尿病前期"
    else:
        result = "血糖正常或轻度异常"
    return result, reason, score

# 注册 SKILL
skill_diabetes = MedicalSkill(
    skill_id="SKILL_DIABETES_V1.2",
    name="糖尿病风险评估",
    evidence=EVIDENCE_DB["diabetes"],
    logic_func=diabetes_logic
)

# 请求体模型
class PatientRequest(BaseModel):
    age: int
    fbs: float
    hba1c: float

# 接口
@app.post("/api/skill/diabetes", summary="糖尿病风险 XAI 评估")
async def diabetes_assess(req: PatientRequest):
    try:
        data = req.dict()
        result = skill_diabetes.execute(data)
        return {"code": 200, "msg": "success", "data": result}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/api/skill/list", summary="获取SKILL列表")
async def skill_list():
    return {
        "code": 200,
        "data": [
            {"skill_id": skill_diabetes.skill_id, "name": skill_diabetes.name}
        ]
    }

if __name__ == "__main__":
    uvicorn.run("main:app", host="0.0.0.0", port=8001, reload=True)

<!DOCTYPE html>
<html lang="zh-CN">
<head>
    <meta charset="UTF-8">
    <title>SKILL-XAI 医疗可解释系统</title>
    <script src="https://cdn.jsdelivr.net/npm/vue@3/dist/vue.global.prod.js"></script>
    <script src="https://cdn.jsdelivr.net/npm/axios/dist/axios.min.js"></script>
    <style>
        .container{max-width:900px;margin:30px auto;padding:20px;font-family:Microsoft YaHei;}
        .card{padding:20px;border-radius:12px;background:#f9f9f9;margin:16px 0;box-shadow:0 2px 8px rgba(0,0,0,0.1);}
        .btn{padding:10px 20px;background:#2E86AB;color:white;border:none;border-radius:8px;cursor:pointer;}
        .input{padding:10px;width:100%;margin:8px 0;border:1px solid #ddd;border-radius:8px;}
        .reason{background:#eef;padding:10px;border-radius:8px;margin:8px 0;}
    </style>
</head>
<body>
<div id="app" class="container">
    <h2>SKILL 可解释医疗 AI 引擎</h2>
    <div class="card">
        <h3>糖尿病风险评估</h3>
        <input v-model.number="age" class="input" placeholder="年龄">
        <input v-model.number="fbs" class="input" placeholder="空腹血糖(mmol/L)">
        <input v-model.number="hba1c" class="input" placeholder="糖化血红蛋白(%)">
        <button @click="runSkill" class="btn">执行评估</button>
    </div>

    <div v-if="result" class="card">
        <h3>可解释报告</h3>
        <p><b>结论：</b>{{result.result}}</p>
        <p><b>触发SKILL：</b>{{result.skill_name}}</p>
        <p><b>医学依据：</b>{{result.evidence}}</p>
        <p><b>置信度：</b>{{result.confidence}}</p>
        <div class="reason">
            <p><b>决策依据链：</b></p>
            <p v-for="(item,i) in result.reason_chain" :key="i">{{i+1}}. {{item}}</p>
        </div>
    </div>
</div>

<script>
const { createApp } = Vue;
createApp({
    data() {
        return { age: 62, fbs: 8.5, hba1c:7.6, result: null }
    },
    methods: {
        async runSkill() {
            try {
                const res = await axios.post("http://127.0.0.1:8001/api/skill/diabetes", {
                    age: this.age, fbs: this.fbs, hba1c: this.hba1c
                });
                this.result = res.data.data;
            } catch(e) {
                alert("请求失败：" + e.message);
            }
        }
    }
}).mount('#app');
</script>
</body>
</html>