AI人工智能代理工作流 AI Agent WorkFlow：在医疗保健中的应用

    # Python 示例：使用scikit-learn库训练一个疾病诊断模型
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.model_selection import train_test_split
    
    # 假设X为特征矩阵，y为标签
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    
    # 创建随机森林分类器
    clf = RandomForestClassifier(n_estimators=100)
    
    # 训练模型
    clf.fit(X_train, y_train)
    
    # 进行预测
    predictions = clf.predict(X_test)
    
    # 评估模型
    print("Accuracy:", clf.score(X_test, y_test))
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # Python 示例：基于协同过滤的药物推荐系统
    from surprise import KNNWithMeans
    from surprise import Dataset, Reader
    
    # 加载数据集
    reader = Reader(rating_scale=(1, 5))
    data = Dataset.load_from_df(df[['user_id', 'drug_id', 'rating']], reader)
    
    # 创建KNN算法
    algo = KNNWithMeans(k=10)
    
    # 训练算法
    algo.fit(data)
    
    # 预测药物评分
    predictions = algo.predict(1, df['drug_id'])
    
    # 推荐药物
    recommended_drugs = predictions.sort('est', ascending=False).head(5)
    print("Recommended Drugs:", recommended_drugs)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # Python 示例：使用pandas进行数据加密和匿名化
    import pandas as pd
    from sklearn.preprocessing import LabelEncoder
    
    # 加载数据
    df = pd.read_csv('medical_data.csv')
    
    # 对敏感数据进行加密
    df['patient_id'] = df['patient_id'].apply(lambda x: encrypt(x))
    
    # 对类别数据进行匿名化
    label_encoder = LabelEncoder()
    df['diagnosis'] = label_encoder.fit_transform(df['diagnosis'])
    
    # 保存加密和匿名化后的数据
    df.to_csv('encrypted_anonymized_data.csv', index=False)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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    # Python 示例：使用统计方法检测数据异常
    from scipy import stats
    
    # 假设df是加载的医疗数据
    z_scores = stats.zscore(df['blood_pressure'])
    
    # 检测异常值
    threshold = 3
    outliers = df[(z_scores > threshold).any(axis=1)]
    
    # 处理异常值
    print("Detected Outliers:", outliers)
    
      
      
      
      
      
      
      
      
      
      
      
    
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    # Python 示例：AI代理记录医生工作流程
    import pickle
    
    # 假设doctor Workflow为医生的工作流程
    doctor_workflow = {'diagnosis': 'COVID-19', 'treatment': 'Isolation'}
    
    # 保存医生工作流程
    with open('doctor_workflow.pkl', 'wb') as f:
    pickle.dump(doctor_workflow, f)
    
    # 加载医生工作流程
    with open('doctor_workflow.pkl', 'rb') as f:
    loaded_workflow = pickle.load(f)
    print("Loaded Workflow:", loaded_workflow)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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