The rapidly expanding corpus of medical research literature presents major challenges in the understanding of previous work, the extraction of maximum information from collected data, and the identification of promising research directions. We present a case for the use of advanced machine learning techniques as an aide in this task and introduce a novel methodology that is shown to be capable of extracting meaningful information from large longitudinal corpora, and of tracking complex temporal changes within it.
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）
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。
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）
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
