基于大模型预测的脑出血全流程诊疗技术方案

一、系统架构设计

技术架构图

二、核心算法实现

1. 多模态数据融合算法伪代码

class MultiModalDataFusion:def __init__(self):self.text_encoder = BioClinicalBERT()  # 医学文本编码器self.image_processor = MedicalImageNet()  # 医学影像处理网络self.time_series_model = TemporalConvNet()  # 时间序列处理网络def fuse_data(self, patient_record):# 文本数据处理text_features = self.text_encoder.encode(patient_record.clinical_notes)# 影像数据处理image_features = self.image_processor.extract_features(patient_record.ct_scan)# 时间序列数据处理ts_features = self.time_series_model.process(patient_record.vital_signs)# 特征融合fused_features = self._attention_fusion(text_features, image_features, ts_features)return fused_featuresdef _attention_fusion(self, *features):# 注意力机制融合算法实现attention_weights = calculate_attention_scores(features)weighted_features = apply_attention_weights(features, attention_weights)return concatenate_features(weighted_features)

2. 风险预测模型实现

class RiskPredictionModel(nn.Module):def __init__(self, input_dim, hidden_dim, output_dim):super(RiskPredictionModel, self).__init__()self.transformer = TransformerEncoder(input_dim, hidden_dim)self.attention = SelfAttention(hidden_dim)self.classifier = nn.Sequential(nn.Linear(hidden_dim, 64),nn.ReLU(),nn.Dropout(0.5),nn.Linear(64, output_dim))def forward(self, x):encoded = self.transformer(x)attended = self.attention(encoded)output = self.classifier(attended)return F.softmax(output, dim=1)

三、关键模块流程图

1. 术前风险预测流程图