基于大模型的尿毒症预测及综合治疗方案技术方案

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一、算法实现伪代码

1. 尿毒症风险预测模型（基于多模态融合Transformer）

# 数据预处理模块
def preprocess_data(clinical_data, imaging_data, genomic_data):
    # 临床数据标准化
    clinical_norm = normalize(clinical_data)
    # 医学影像特征提取（CNN特征）
    imaging_features = extract_cnn_features(imaging_data)
    # 基因组数据编码
    genomic_encoded = one_hot_encode(genomic_data)
    # 多模态特征融合
    fused_features = merge_features([clinical_norm, imaging_features, genomic_encoded])
    return fused_features

# 模型训练与推理
class UreaNet(nn.Module):
    def __init__(self):
        super(UreaNet, self).__init__()
        self.transformer = TransformerEncoder(...)  # 多层Transformer编码器
        self.risk_head = nn.Linear(..., 1)          # 风险评分输出
        self.stage_head = nn.Linear(..., 5)         # 分期分类输出（5个阶段）

    def forward(self, x):
        x = self.transformer(x)
        risk_score = self.risk_head(x)
        stage_prob = F.softmax(self.stage_head(x), dim=1)
        return risk_score, stage_prob

# 训练流程
def train_model(train_data):
    for batch in train_data:
        features = preprocess_data(batch["clinical"], batch["imaging"], batch["genomic"])
        risk_true, stage_true = batch["risk_label"], batch["stage_label"]
        
        optimizer.zero_grad()
        risk_pred, stage_pred = model(features)
        
        loss_risk = L1Loss()(risk_pred, risk_true)
        loss_stage = CrossEntropyLoss()(stage_pred, stage_true)
        loss = loss_risk + loss_stage
        loss.backward()
        optimizer.step()

# 推理流程
def predict(patient_data):
    features = preprocess_data(patient_data["clinical"], patient_data["imaging"], patient_data["genomic"])
    risk_score, stage_prob = model(features)
    return risk_score.item(), torch.argmax(stage_prob).item()

2. 动态治疗方案生成算法

# 治疗方案决策树（规则引擎）
def generate_treatment_plan(risk_score, stage, comorbidities):
    if risk_score >= 0.8 and stage == 4:
        return "立即透析+肾移植评估"
    elif stage in [3,4] and risk_score > 0.6:
        return "强化药物治疗+血液透析准备"
    else:
        return default_treatment(comorbidities)

# 个性化药物推荐（基于案例库匹配）
def default_treatment(comorbidities):
    candidate_drugs = query_case_library(comorbidities)
    drug_scores = []
    for drug in candidate_drugs:
        score = calculate_suitability(drug, patient_profile)
        drug_scores.append((drug, score))
    sorted_drugs = sorted(drug_scores, key=lambda x: x[1], reverse=True)
    return sorted_drugs[:3]  # 返回前3推荐药物

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二、系统模块流程图

1. 尿毒症智能预测系统流程