基于大模型的短暂性脑缺血发作（TIA）全流程预测与干预系统技术方案

一、系统架构总览

二、核心模块详细设计

1. 术前预测系统

算法伪代码：

# Transformer-TCN 融合模型
class HybridModel(nn.Module):def __init__(self):# 时序特征处理self.tcn = TemporalConvNet(num_inputs=128, num_channels=[64, 32])# 多模态融合self.transformer = TransformerEncoder(layers=4, d_model=256,heads=8)# 风险预测头self.risk_head = nn.Sequential(nn.Linear(256, 128),nn.ReLU(),nn.Linear(128, 3)  # 输出3类风险等级def forward(self, clinical, imaging, temporal):# 临床数据处理clinical_feat = self.clinical_net(clinical)# 影像特征提取img_feat = self.resnet3d(imaging)# 时序特征处理temp_feat = self.tcn(temporal)# 多模态融合fused = torch.cat([clinical_feat, img_feat, temp_feat], dim=1)# Transformer编码encoded = self.transformer(fused)# 风险预测risk = self.risk_head(encoded[:, -1])return risk# 手术方案生成器
def generate_surgery_plan(risk_score, stenosis_map):if risk_score > 0.85:plan = "紧急CEA/CAS手术"elif risk_score > 0.6:plan = "48小时内血管介入"else:plan = "药物治疗+监测"# 基于狭窄定位生成细节if stenosis_map["ICA"] > 70%:plan += " + 颈动脉支架"if stenosis_map["MCA"] > 50%:plan += " + 抗凝强化"return plan

2. 术中决策系统

实时推理伪代码：

def intraoperative_decision(vitals):# 实时特征提取features = extract_features(eeg = vitals["eeg_power"],bp = [vitals["systolic"], vitals["diastolic"]],spo2 = vitals["oxygen_sat"])# ONNX加速推理session = ort.InferenceSession("intraop_model.onnx")inputs = {"input": features}outputs = session.run(None, inputs)# 解析输出risk = outputs[0][0]action = outputs[1][0]# 执行决策if action == "ADJUST_ANESTHESIA":adjust_anesthesia_pump(risk * 0.2)  # 剂量调整系数elif action == "ALTER_PATH":update_surgical_navigation(risk)# 麻醉调控算法
def adjust_anesthesia_pump(adjustment_factor):current_dose = get_current_dose()new_dose = current_dose * (1 + adjustment_factor)# 安全范围限制new_dose = clamp(new_dose, MIN_DOSE, MAX_DOSE)send_to_pump(new_dose)

3. 术后管理系统