patient_id diagnosistreatmentdoctor_notes0 101DiabetesInsulin Patient responds well to treatment1 102 HypertensionLisinoprilBlood pressure needs monitoring2 103Coronary Artery Disease Aspirin Recommend lifestyle changes

from datetime import datetime
# 原始事件记录event_data = ['12-08-2021', '08/12/2021', '2021.08.12']
# 标准化处理standardized_dates = [datetime.strptime(date, '%d-%m-%Y').strftime('%Y-%m-%d') for date in event_data]print(standardized_dates)

['2021-08-12', '2021-08-12', '2021-08-12']

# 假设我们有一系列文档，其中部分与糖尿病有关documents = ["This research discusses the effects of insulin on diabetes treatment.","This paper explores hypertension treatment methods.","An analysis on the causes of coronary artery disease."]
# 聚焦处理，筛选出与糖尿病相关的文档focused_docs = [doc for doc in documents if "diabetes" in doc.lower()]print(focused_docs)

['This research discusses the effects of insulin on diabetes treatment.']

from transformers import BertForQuestionAnswering, BertTokenizer, Trainer, TrainingArguments
# 加载预训练的BERT模型和tokenizermodel = BertForQuestionAnswering.from_pretrained('bert-base-uncased')tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
# 假设我们有医疗领域的问答数据集train_dataset = ...# 数据加载代码省略
# 微调模型training_args = TrainingArguments(output_dir='./results',num_train_epochs=3,per_device_train_batch_size=16,save_steps=10_000,save_total_limit=2,)
trainer = Trainer(model=model,args=training_args,train_dataset=train_dataset,)
trainer.train()

from transformers import Conversation, ConversationalPipeline
# 假设我们使用一个训练好的生成模型pipeline = ConversationalPipeline(model=model)
# 创建对话conversation = Conversation("What are the symptoms of diabetes?")conversation.add_user_input("How is it diagnosed?")
# 模型生成答案response = pipeline(conversation)print(response)

prompt = "Based on the research papers on diabetes treatment, explain the role of insulin and cite the relevant sources."response = model.generate(prompt)print(response)

from sentence_transformers import SentenceTransformer, util
# 加载Sentence-BERT模型model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
# 知识库文档documents = ["Insulin is important for diabetes treatment.",  "Hypertension is treated with Lisinopril.",  "Aspirin is used for coronary artery disease."]
# 用户问题query = "What is used to treat diabetes?"
# 将文档和查询向量化doc_embeddings = model.encode(documents)query_embedding = model.encode(query)
# 计算相似度similarities = util.cos_sim(query_embedding, doc_embeddings)most_similar_doc = documents[similarities.argmax()]print(most_similar_doc)

'Insulin is important for diabetes treatment.'

# 初步检索结果documents = [{"text": "Older study on insulin", "date": "2010", "citations": 50},{"text": "Recent study on insulin", "date": "2022", "citations": 10},]
# 基于业务规则重新排序reranked_docs = sorted(documents, key=lambda x: x['date'], reverse=True)print(reranked_docs)

[{'text': 'Recent study on insulin', 'date': '2022', 'citations': 10},  {'text': 'Older study on insulin', 'date': '2010', 'citations': 50}]

from transformers import pipeline
# 使用一个简单的问答改写模型question_rewriter = pipeline("text2text-generation", model="t5-small")
# 用户问题original_question = "How can I manage diabetes?"
# 改写问题rewritten_question = question_rewriter(original_question)print(rewritten_question)

# 假设我们通过生成模型计算置信度def can_answer(query, docs):# 模拟模型返回的置信度confidence = model.predict_confidence(query, docs)return confidence > 0.5
# 判断是否能回答if can_answer("What is diabetes?", documents):print("Generating answer...")else:print("Unable to answer the question.")