基于大模型的结节性甲状腺肿智能诊疗系统技术方案

一、核心算法实现伪代码

1. 多模态数据预处理算法

# 数据标准化与特征融合伪代码
def preprocess_data(ultrasonic_images, clinical_features):# 图像标准化处理normalized_images = []for img in ultrasonic_images:img = resize(img, target_size=(224, 224))  # 统一图像尺寸img = normalize(img, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])  # 标准化normalized_images.append(img)# 临床特征归一化normalized_features = (clinical_features - np.mean(clinical_features, axis=0)) / np.std(clinical_features, axis=0)# 特征融合fused_features = np.concatenate((normalized_images, normalized_features), axis=1)return fused_features

2. 结节良恶性预测模型

# 基于Transformer的预测模型伪代码
class NoduleClassificationModel(nn.Module):def __init__(self):super(NoduleClassificationModel, self).__init__()self.image_encoder = ImageTransformer(input_channels=3, num_classes=2)self.clinic_encoder = ClinicalFeatureEncoder(input_dim=10)  # 假设10个临床特征self.fusion_layer = nn.Linear(512 + 256, 128)  # 特征融合层self.output_layer = nn.Linear(128, 2)  # 二分类输出def forward(self, image_data, clinic_data):img_feat = self.image_encoder(image_data)clinic_feat = self.clinic_encoder(clinic_data)fused_feat = torch.cat((img_feat, clinic_feat), dim=1)x = F.relu(self.fusion_layer(fused_feat))return self.output_layer(x)

3. 手术风险评估算法

# 基于XGBoost的风险预测模型伪代码
def train_surgery_risk_model(training_data):# 特征工程：构造组合特征training_data['age_tsh_interaction'] = training_data['age'] * training_data['TSH']# 定义模型参数params = {'objective': 'multi:softprob','num_class': 5,  # 低/中低/中/中高/高风险五级分类'eval_metric': 'mlogloss'}# 训练模型model = xgboost.train(params, training_data, num_boost_round=100)return model

二、系统模块流程图（Mermaid格式）

1. 数据预处理流程