1. 數據預處理與特征工程

偽代碼 - 數據清洗與特征處理

def DataPreprocessing(raw_data):# 數據清洗cleaned_data = RemoveMissingValues(raw_data)  # 刪除缺失值超過30%的樣本cleaned_data = FilterOutliers(cleaned_data)    # 基于IB檢驗去除異常值# 特征歸一化normalized_data = MinMaxScaler(cleaned_data)   # 線性歸一化到[0,1]# 特征編碼encoded_data = OneHotEncode(normalized_data)   # 對分類變量進行獨熱編碼# 多模態特征融合fusion_data = Concatenate(encoded_data, ImageFeatures(CT_scans), TextFeatures(medical_notes))  # 合并臨床數據、影像和文本特征return fusion_data

數據預處理流程圖

2. 大模型構建與訓練

偽代碼 - 模型訓練

def TrainStonePredictionModel(preprocessed_data):# 模型架構定義model = TransformerModel(input_dim=feature_length,num_layers=12,heads=8,dropout=0.3)# 訓練配置train_loader, val_loader = SplitDataset(preprocessed_data, ratio=0.8)criterion = CrossEntropyLoss()optimizer = AdamOptimizer(learning_rate=1e-4)# 訓練循環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

模型訓練流程圖