1. 数据预处理与特征工程

伪代码 - 数据清洗与特征处理

def DataPreprocessing(raw_data):# 数据清洗cleaned_data = RemoveMissingValues(raw_data)  # 删除缺失值超过30%的样本cleaned_data = FilterOutliers(cleaned_data)    # 基于IB检验去除异常值# 特征归一化normalized_data = MinMaxScaler(cleaned_data)   # 线性归一化到[0,1]# 特征编码encoded_data = OneHotEncode(normalized_data)   # 对分类变量进行独热编码# 多模态特征融合fusion_data = Concatenate(encoded_data, ImageFeatures(CT_scans), TextFeatures(medical_notes))  # 合并临床数据、影像和文本特征return fusion_data

数据预处理流程图

2. 大模型构建与训练

伪代码 - 模型训练

def TrainStonePredictionModel(preprocessed_data):# 模型架构定义model = TransformerModel(input_dim=feature_length,num_layers=12,heads=8,dropout=0.3)# 训练配置train_loader, val_loader = SplitDataset(preprocessed_data, ratio=0.8)criterion = CrossEntropyLoss()optimizer = AdamOptimizer(learning_rate=1e-4)# 训练循环for epoch in range(100):for batch in train_loader:predictions = model.forward(batch)loss = criterion(predictions, batch.labels)optimizer.backward(loss)optimizer.step()val_loss = ValidateModel(model, val_loader)SaveCheckpoint(model, val_loss)return model

模型训练流程图