我们报告我们的努力，以确定敏感信息，数据项的子集列出的健康保险便携性和责任，从医疗文本使用自然语言处理和机器学习技术的最新进展。我们用Word2Vec的一个变体（称为连续词袋（Continuous Bag Of Words，CBOW））学习的高维连续向量表示单词。我们将单词向量输入到一个简单的神经网络中，该网络具有长短期记忆（LSTM）架构。在没有任何手动提取特征的尝试的情况下，考虑到我们的医疗数据集太小，无法送入神经网络，我们获得了有希望的结果。结果让我们兴奋地想到，通过精确的参数调整和其他可能的改进，可以实现更大规模的项目。
英文摘要：
We report our effort to identify the sensitive information, subset of data items listed by HIPAA (Health Insurance Portability and Accountability), from medical text using the recent advances in natural language processing and machine learning techniques. We represent the words with high dimensional continuous vectors learned by a variant of Word2Vec called Continous Bag Of Words (CBOW). We feed the word vectors into a simple neural network with a Long Short-Term Memory (LSTM) architecture. Without any attempts to extract manually crafted features and considering that our medical dataset is too small to be fed into neural network, we obtained promising results. The results thrilled us to think about the larger scale of the project with precise parameter tuning and other possible improvements.
DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4
The University of Georgia（佐治亚大学）；Lehigh University（莱恩大学）；The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Massachusetts General Hospital and Harvard Medical School（麻省总医院和哈佛医学院）；Mayo Clinic（梅奥诊所）；ShanghaiTech University（上海科技大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能科技有限公司）；Shanghai Clinical Research and Trial Center（上海临床研究与试验中心）
Prompt engineering paradigms for medical applications: scoping review and recommendations for better practices
Geneva University Hospitals（日内瓦大学医院）；University of Geneva（日内瓦大学）；Université Paris-Saclay（巴黎萨克雷大学）；CNRS（法国国家科学研究中心）；Sorbonne Université（索邦大学）；INSERM（法国国家健康与医学研究院）；Université Sorbonne Paris-Nord（索邦巴黎北大学）
Efficiently Reusing Natural Language Processing Models for Phenotype-Mention Identification in Free-text Electronic Medical Records: Methodology Study
University of Edinburgh（爱丁堡大学）；Health Data Research（健康数据研究中心）；Nanjing University of Information Science & Technology（南京信息工程大学）；King’s College London（伦敦国王学院）；South London and Maudsley NHS Foundation Trust（南伦敦和莫兹利国民保健信托基金会）；The University of Melbourne（墨尔本大学）；Health Data Research UK（英国健康数据研究中心）；University College London（伦敦大学学院）
From Text to Multimodality: Exploring the Evolution and Impact of Large Language Models in Medical Practice
Kyoto University（京都大学）；Georgia Institute of Technology（佐治亚理工学院）；National Taiwan Normal University（台湾师范大学）；Indiana University（印第安纳大学）；Hong Kong University of Science（香港科学大学）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Cornell University（康奈尔大学）；University of Liverpool（利物浦大学）；University of Edinburgh（爱丁堡大学）；Zhejiang University（浙江大学）；Purdue University（Purdue 大学）；Emory University, Atlanta, GA, USA（埃默里大学）；West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China（西京生物大数据中心，四川大学西京医院，成都，中国）
FocusMed: A Large Language Model-based Framework for Enhancing Medical Question Summarization with Focus Identification
1 College of Computer Science；Technology, Dalian University of Technology, Dalian, China；2 Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital \& Institute, Shenyang, China
MedRCube: A Multidimensional Framework for Fine-Grained and In-Depth Evaluation of MLLMs in Medical Imaging
School of Data Science, Fudan University（复旦大学数据科学学院）；Shanghai Innovation Institute（上海创新研究院）；School of Integrated Circuits, Zhejiang University（浙江大学集成电路学院）；School of Software Engineering, Huazhong University of Science and Technology（华中科技大学软件工程学院）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）
