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一、算法实现伪代码

1. 数据预处理模块

def preprocess_data(raw_data):  # 数据清洗：去除缺失值和异常值  cleaned_data = remove_missing_and_abnormal(raw_data)  # 特征归一化  normalized_data = normalize_features(cleaned_data)  # 特征提取（临床指标+影像特征+时序特征）  clinical_features = extract_clinical_features(normalized_data)  image_features = extract_image_features(normalized_data["images"])  temporal_features = generate_temporal_features(normalized_data["time_series"])  # 合并特征  combined_features = merge_features([clinical_features, image_features, temporal_features])  return combined_features  

2. 大模型训练与预测

def train_predictive_model(training_data, validation_data):  # 模型初始化（示例：Transformer+GNN混合架构）  model = HybridModel(input_dim=training_data.shape[1], num_classes=2)  # 二分类：患病/健康  # 训练循环  for epoch in range(MAX_EPOCHS):  for batch in training_data:  # 前向传播  logits = model.forward(batch)  # 损失计算（交叉熵+正则化）  loss = compute_loss(logits, batch.labels)  # 反向传播与优化  model.backward(loss)  model.optimizer.step()  # 验证集评估  val_loss = evaluate_model(model, validation_data)  if val_loss < best_loss:  best_loss = val_loss  save_model(model, "best_model.pth")  return model  def predict(model, input_data):  # 数据预处理  processed_data = preprocess_data(input_data)  # 模型推理  logits = model.forward(processed_data)  # 概率转换与阈值判断  probabilities = softmax(logits)  prediction = threshold_prediction(probabilities, threshold=0.5)  return prediction  

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二、系统模块详细流程图

1. 数据预处理系统