智能Agent在医疗健康领域的应用

    # 1. 构建医学知识图谱
    from py2neo import Graph, Node, Relationship
    
    graph = Graph("bolt://localhost:7687", auth=("neo4j", "password"))
    
    # 创建疾病、症状、检查项目等实体节点
    disease_node = Node("Disease", name="感冒")
    symptom_node1 = Node("Symptom", name="发烧")
    symptom_node2 = Node("Symptom", name="咳嗽")
    check_node = Node("Check", name="体温检查")
    
    # 创建实体之间的关系
    rel1 = Relationship(disease_node, "MANIFESTS", symptom_node1)
    rel2 = Relationship(disease_node, "MANIFESTS", symptom_node2)
    rel3 = Relationship(disease_node, "REQUIRES", check_node)
    
    graph.create(disease_node, symptom_node1, symptom_node2, check_node, rel1, rel2, rel3)
    
    # 2. 症状信息抽取和知识图谱推理
    def diagnose(symptoms):
    # 从用户输入中抽取症状信息
    user_symptoms = [Node("Symptom", name=s) for s in symptoms]
    
    # 在知识图谱中查找匹配的疾病
    possible_diseases = []
    for symptom in user_symptoms:
        diseases = graph.run(
            "MATCH (d:Disease)-[:MANIFESTS]-(s:Symptom) WHERE s.name = $symptom_name RETURN d",
            symptom_name=symptom["name"]
        ).data()
        possible_diseases.extend(diseases)
    
    # 返回可能的疾病诊断结果
    return list({d["d"]["name"] for d in possible_diseases})
    
    # 测试
    symptoms = ["发烧", "咳嗽"]
    print(diagnose(symptoms))  # 输出: ['感冒']
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-18/Q7EpfHcRsSLwzOhyFMVd64IgP1ev.png)

    import gym
    import numpy as np
    from collections import deque
    from keras.models import Sequential
    from keras.layers import Dense
    from keras.optimizers import Adam
    
    # 定义健康管理环境
    class HealthManagementEnv(gym.Env):
    def __init__(self, user_profile):
        self.user_profile = user_profile
        self.current_state = user_profile
        self.action_space = ['diet_plan_A', 'diet_plan_B', 'exercise_plan_A', 'exercise_plan_B']
        self.observation_space = self.user_profile.shape
    
    def step(self, action):
        # 根据采取的健康管理策略,计算下一个状态和奖励
        next_state, reward = self.update_state_and_reward(action)
        done = self.is_goal_achieved(next_state)
        return next_state, reward, done, {}
    
    def reset(self):
        self.current_state = self.user_profile
        return self.current_state
    
    # 其他环境相关的方法...
    
    # 定义强化学习智能Agent
    class HealthManagementAgent:
    def __init__(self, env):
        self.env = env
        self.model = self.build_model()
        self.target_model = self.build_model()
        self.replay_buffer = deque(maxlen=2000)
        self.gamma = 0.95
        self.epsilon = 1.0
        self.epsilon_min = 0.01
        self.epsilon_decay = 0.995
        self.learning_rate = 0.001
    
    def build_model(self):
        model = Sequential()
        model.add(Dense(24, input_dim=self.env.observation_space.shape[0], activation='relu'))
        model.add(Dense(24, activation='relu'))
        model.add(Dense(len(self.env.action_space), activation='linear'))
        model.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))
        return model
    
    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return np.random.choice(self.env.action_space)
        act_values = self.model.predict(state)
        return self.env.action_space[np.argmax(act_values[0])]
    
    def replay(self, batch_size):
        minibatch = random.sample(self.replay_buffer, batch_size)
        for state, action, reward, next_state, done in minibatch:
            target = reward
            if not done:
                target = (reward + self.gamma * np.amax(self.target_model.predict(next_state)[0]))
            target_f = self.model.predict(state)
            target_f[0][self.env.action_space.index(action)] = target
            self.model.fit(state, target_f, epochs=1, verbose=0)
    
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay
    
    def train(self, num_episodes):
        for episode in range(num_episodes):
            state = self.env.reset()
            state = np.reshape(state, [1, self.env.observation_space.shape[0]])
            done = False
            while not done:
                action = self.act(state)
                next_state, reward, done, _ = self.env.step(action)
                next_state = np.reshape(next_state, [1, self.env.observation_space.shape[0]])
                self.replay_buffer.append((state, action, reward, next_state, done))
                state = next_state
                if len(self.replay_buffer) > batch_size:
                    self.replay(batch_size)
    
    # 使用示例
    user_profile = np.array([150, 30, 80, 1.75, 'sedentary'])
    env = HealthManagementEnv(user_profile)
    agent = HealthManagementAgent(env)
    agent.train(1000)
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-18/vbfwX0FxEi167TCtd5HkNnO8K2yo.png)