基于大模型的急性出血坏死性胰腺炎预测技术方案

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一、算法实现伪代码

1. 数据预处理与特征工程

# 数据清洗与归一化
def preprocess_data(raw_data):
    # 处理缺失值
    cleaned_data = fill_missing_values(raw_data)
    # 标准化数值特征
    normalized_data = normalize(cleaned_data)
    # 编码分类特征
    encoded_data = one_hot_encode(normalized_data)
    return encoded_data

# 特征选择（基于医学知识+模型重要性）
def feature_selection(data):
    medical_features = ["CRP", "WBC", "CT_severity_score"]  # 医学特征
    model_features = select_important_features(data)        # 模型筛选特征
    return medical_features + model_features

2. 大模型训练（以Transformer为例）

# 模型定义（简化版）
class PancreatitisPredictor(Transformer):
    def __init__(self):
        super().__init__()
        self.embedding = nn.Linear(feature_num, 768)
        self.transformer_layers = nn.ModuleList([
            TransformerLayer(hidden_size=768, num_heads=12) for _ in range(12)
        ])
        self.output = nn.Linear(768, 3)  # 输出类别：轻度/重度/并发症风险

    def forward(self, x):
        x = self.embedding(x)
        for layer in self.transformer_layers:
            x = layer(x)
        return self.output(x)

# 训练流程
def train_model(train_data, val_data):
    model = PancreatitisPredictor()
    for epoch in range(EPOCHS):
        for batch in train_data:
            predictions = model(batch["features"])
            loss = compute_loss(predictions, batch["labels"])
            backpropagate(loss)
        validate_model(model, val_data)
    return model

3. 实时预测与动态调整

# 术中实时预测
def realtime_prediction(model, new_data):
    processed_data = preprocess_stream(new_data)  # 流式数据处理
    risk_score = model.predict(processed_data)
    if risk_score > THRESHOLD:
        alert_surgeon("高风险预警")
    return risk_score

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二、模块流程图

1. 术前预测流程

采集病史/症状/检查结果

数据清洗与归一化

特征工程

输入大模型

输出预测结果

生成手术方案

麻醉风险评估

制定麻醉计划

2. 术中动态决策流程