医疗健康领域：机器学习技术的深入渗透与未来愿景之探寻

 import pandas as pd  
    
 from sklearn.model_selection import train_test_split  
    
 from sklearn.linear_model import LogisticRegression  
    
 from sklearn.metrics import accuracy_score  
    
   
    
 # 加载数据  
    
 data = pd.read_csv('heart_disease.csv')  
    
 X = data.drop('disease', axis=1)  # 特征数据  
    
 y = data['disease']  # 标签数据  
    
   
    
 # 划分训练集和测试集  
    
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)  
    
   
    
 # 创建逻辑回归模型  
    
 model = LogisticRegression()  
    
   
    
 # 训练模型  
    
 model.fit(X_train, y_train)  
    
   
    
 # 预测测试集结果  
    
 y_pred = model.predict(X_test)  
    
   
    
 # 评估模型准确率  
    
 accuracy = accuracy_score(y_test, y_pred)  
    
 print(f'模型准确率: {accuracy:.2f}')

 import tensorflow as tf  
    
 from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input  
    
 from tensorflow.keras.preprocessing import image  
    
 from tensorflow.keras.models import Model  
    
 from tensorflow.keras.layers import Dense, GlobalAveragePooling2D  
    
 import numpy as np  
    
   
    
 # 加载预训练的ResNet50模型，不包括顶层  
    
 base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))  
    
   
    
 # 添加全局平均池化层  
    
 x = base_model.output  
    
 x = GlobalAveragePooling2D()(x)  
    
   
    
 # 添加一个全连接层并输出预测结果（假设我们有两个类别：肺癌和非肺癌）  
    
 predictions = Dense(1, activation='sigmoid')(x)  
    
   
    
 # 构建新的模型  
    
 model = Model(inputs=base_model.input, outputs=predictions)  
    
   
    
 # 冻结预训练模型的权重  
    
 for layer in base_model.layers:  
    
     layer.trainable = False  
    
   
    
 # 编译模型  
    
 model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])  
    
   
    
 # 加载并预处理图像（这里只是一个框架，你需要自己准备图像数据）  
    
 img_path = 'path_to_your_image.jpg'  
    
 img = image.load_img(img_path, target_size=(224, 224))  
    
 x = image.img_to_array(img)  
    
 x = np.expand_dims(x, axis=0)  
    
 x = preprocess_input(x)  
    
   
    
 # 进行预测  
    
 preds = model.predict(x)  
    
 print(f'肺癌预测概率: {preds[0][0]:.2f}')

 import torch  
    
 import torch.nn.functional as F  
    
 from torch_geometric.nn import GCNConv  
    
 from torch_geometric.data import Data  
    
   
    
 class DrugInteractionPredictor(torch.nn.Module):  
    
     def __init__(self, num_features, num_drugs, hidden_channels):  
    
     super(DrugInteractionPredictor, self).__init__()  
    
     self.conv1 = GCNConv(num_features, hidden_channels)  
    
     self.conv2 = GCNConv(hidden_channels, num_drugs)  
    
   
    
     def forward(self, data):  
    
     x, edge_index = data.x, data.edge_index  
    
   
    
     x = self.conv1(x, edge_index)  
    
     x = F.relu(x)  
    
     x = F.dropout(x, training=self.training)  
    
     x = self.conv2(x, edge_index)  
    
   
    
     return F.sigmoid(x)  # Assuming we are predicting interaction probabilities  
    
   
    
 # 假设我们有以下模拟数据  
    
 num_drugs = 100  # 假设有100种药物  
    
 num_features = 10  # 假设每种药物有10个特征  
    
 hidden_channels = 32  # 隐藏层通道数  
    
   
    
 # 创建模拟数据（在实际应用中，你需要从数据集中加载数据）  
    
 edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long)  # 药物间相互作用关系  
    
 x = torch.randn((num_drugs, num_features))  # 药物特征  
    
 data = Data(x=x, edge_index=edge_index)  
    
   
    
 # 初始化模型  
    
 model = DrugInteractionPredictor(num_features, num_drugs, hidden_channels)  
    
   
    
 # 假设我们有一个优化器和损失函数  
    
 optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)  
    
 criterion = torch.nn.BCELoss()  # 二元交叉熵损失，假设我们在预测相互作用概率  
    
   
    
 # 训练循环（此处省略）  
    
 # ...  
    
   
    
 # 预测药物相互作用  
    
 predictions = model(data)  
    
 print(predictions)  # 输出每个药物对的相互作用概率

 import websocket  
    
 import json  
    
 import time  
    
   
    
 # 假设我们有一个WebSocket服务器用于接收患者数据  
    
 def on_message(ws, message):  
    
     # 收到患者数据，处理并进行分析  
    
     data = json.loads(message)  
    
     # 在这里，你可以调用你的机器学习模型来处理数据  
    
     # ...  
    
     print(f"Received patient data: {data}")  
    
   
    
 # 连接到WebSocket服务器  
    
 ws = websocket.WebSocketApp("wss://your-websocket-server.com/ws",  
    
                         on_message = on_message,  
    
                         on_error = on_error,  
    
                         on_close = on_close)  
    
 ws.on_open = on_open  
    
 ws.run_forever()  
    
   
    
 # 注意：这里只是WebSocket的基本用法，实际应用中你可能需要更复杂的逻辑来处理数据