实现人工智能辅助医疗翻译：促进医疗国际化

    from hashlib import sha256
    
    class Block:
    def __init__(self, index, timestamp, data, previous_hash):
        self.index = index
        self.timestamp = timestamp
        self.data = data
        self.previous_hash = previous_hash
        self.hash = self._calc_hash()
    
    def _calc_hash(self):
        block_str = str(self.index) + str(self.timestamp) + str(self.data) + str(self.previous_hash)
        return sha256(block_str.encode()).hexdigest()
    
    class MedicalRecord:
    def __init__(self):
        self.chain = [Block(0, datetime.datetime.now(), "Genesis Block", "0")]
    
    def add_record(self, record):
        new_block = Block(len(self.chain), datetime.datetime.now(), record, self.last_block().hash)
        self.chain.append(new_block)
    
    def last_block(self):
        return self.chain[-1]
    
    def get_block(self, hash):
        for block in self.chain:
            if block.hash == hash:
                return block
    
        return None
    
    @property
    def size(self):
        return len(self.chain)
    
    mr = MedicalRecord()
    print("Before adding records:", mr.size)
    mr.add_record("Patient's personal medical history") # or patient ID could be used as the key to access their records 
    mr.add_record("Doctor's prescription list")
    mr.add_record("Diagnostic images of patient's illness and symptoms")
    print("After adding records:", mr.size)
    
    # Lookup a record based on its SHA256 value (which is unique per record)
    lookup_key = "..." # assume this is retrieved from the user
    found_block = mr.get_block(sha256(lookup_key.encode()).hexdigest())
    if found_block:
    print("Found record at index {} with data {}".format(found_block.index, found_block.data))
    else:
    print("No matching record was found.")

    import spacy
    
    nlp = spacy.load('en_core_web_sm')
    
    def extract_entities(text):
    doc = nlp(text)
    entities = []
    for ent in doc.ents:
        entity_dict = {
            'entity': ent.text, 
            'label': ent.label_,
           'start': ent.start_char,
            'end': ent.end_char}
        entities.append(entity_dict)
    
    return entities
    
    text = """"In contrast to other nuclear power plants, American Thor conveys heat and electricity directly to consumers, making it the cleanest and most environmentally responsible energy source available today." -Wikipedia"""
    extracted_entities = extract_entities(text)
    for e in extracted_entities:
    print(e)

    import re
    from sklearn.cluster import KMeans
    
    def tokenize(doc):
    doc = re.sub(r'\(|\)|\:|\-|\;|"|\/|\.|,|\?|\!|\'|\`', '', doc)   # remove special characters
    tokens = doc.lower().split()                                # convert to lowercase and split into words
    return tokens
    
    def cluster(docs):
    tokenized_docs = [tokenize(doc) for doc in docs]              # tokenize each document
    kmeans = KMeans(n_clusters=num_clusters).fit(tokenized_docs)    # perform clustering using K-Means algorithm
    centroids = kmeans.cluster_centers_.tolist()                 # retrieve clusters' centroids
    labels = kmeans.labels_.tolist()                              # retrieve documents' cluster labels
    topic_docs = defaultdict(list)                                 # create dictionary to store topics and corresponding documents
    for i in range(num_clusters):                                 
        topic_words = set([word for word in centroids[i]])          # select only those words that are part of the current topic
        for j in range(len(docs)):                                  
            if labels[j] == i:                                     
                topic_docs[i].append((j, [word for word in tokenized_docs[j] if word in topic_words]))
    
    return dict(topic_docs)                                         # convert dictionary back to regular format 
    
    num_clusters = 3                                                  # number of clusters to generate
    corpus = ["This is an example document.", 
         "Another sample document about coronavirus.",
         "A third document discussing policies related to social distancing."]
    
    topic_docs = cluster(corpus)                                       # generate topics and assign documents accordingly
    for t, d in topic_docs.items():
    print("Topic {}:".format(t+1))
    for doc in d:
        print("- Document {} contains keywords: ".format(doc[0]+1)+ ",".join(doc[1]))

    import networkx as nx
    
    def build_path_graph(symptom_to_condition_map):
    G = nx.DiGraph()
    root = 0
    for condition, symptoms in symptom_to_condition_map.items():
        node = max(G.nodes) + 1
        G.add_node(node, label=condition)
        parent_node = root
        for s in symptoms:
            child_node = min(G.nodes) - 1
            G.add_edge(parent_node, child_node, weight=1, label="has symptom")
            G.add_node(child_node, label=s)
            parent_node = child_node
    
    return G
    
    def calculate_path(G, target_symptom):
    nodes = [(node, attr['weight']) for node, attr in G.degree()]
    symptom_node = next(node for node, degree in nodes if attr['label'] == target_symptom)
    
    path = nx.shortest_path(G, source=root, target=symptom_node, weight='weight')
    weighted_edges = [(u, v, attr['weight'], attr['label']) for u, v, attr in G.edges(data=True)]
    total_cost = sum([w for u, v, w, l in weighted_edges])
    
    return {'path': path, 'total_cost': total_cost}
    
    # Example usage
    symptom_to_condition_map = {"Fever": ["Cough"],
                            "Headache": ["Sore throat"]}
    G = build_path_graph(symptom_to_condition_map)
    result = calculate_path(G, "Headache")
    print("Path:", result['path'])
    print("Total cost:", result['total_cost'])

    import os
    import time
    from wechatpy import parse_message
    from wechatpy.client.api import WeChatClient
    from wechatpy.crypto import WeChatCrypto
    
    def handle_message(msg):
    try:
        text = msg.content.strip().lower()
        if text == 'help me':
            client.send_text(msg.source, "What can I do for you?")
        elif text == 'check my records':
            results = check_records(msg.source)
            client.send_text(msg.source, results)
        else:
            client.send_text(msg.source, "Sorry, I don't understand your request.")
    except Exception as e:
        print("Error handling message:", e)
    
    def check_records(user_id):
    pass  # code to query database and return results
    
    # Set up encryption parameters
    encoding_aes_key = os.environ["WECHATPY_ENCODING_AES_KEY"]
    token = os.environ["WECHATPY_TOKEN"]
    app_id = os.environ["WECHATPY_APPID"]
    
    # Create crypto object
    crypto = WeChatCrypto(token, encoding_aes_key, app_id)
    
    # Initialize client with credentials
    client = WeChatClient("YOUR_WECHAT_ACCOUNT_OPENID", "YOUR_WECHAT_ACCOUNT_SECRET")
    
    # Start listening to messages
    while True:
    time.sleep(1)
    try:
        message = client.fetch_messages()
        if not message:
            continue
    
        encrypt_xml = message.encrypt_xml
        plaintext, nonce, msg_signature = crypto.decrypt_message(encrypt_xml)
        xml_obj = parse_message(plaintext)
        handle_message(xml_obj)
    except Exception as e:
        print("Error while fetching message:", e)