The Clinical E-Science Framework (CLEF) project was used to extract important information from medical texts by building a system for the purpose of clinical research, evidence-based healthcare and genotype-meets-phenotype informatics. The system is divided into two parts, one part concerns with the identification of relationships between clinically important entities in the text. The full parses and domain-specific grammars had been used to apply many approaches to extract the relationship. In the second part of the system, statistical machine learning (ML) approaches are applied to extract relationship. A corpus of oncology narratives that hand annotated with clinical relationships can be used to train and test a system that has been designed and implemented by supervised machine learning (ML) approaches. Many features can be extracted from these texts that are used to build a model by the classifier. Multiple supervised machine learning algorithms can be applied for relationship extraction. Effects of adding the features, changing the size of the corpus, and changing the type of the algorithm on relationship extraction are examined. Keywords: Text mining; information extraction; NLP; entities; and relations.
Lung Cancer Concept Annotation from Spanish Clinical Narratives
Centro de Tecnología Biomédica, Universidad Politécnica de Madrid（马德里理工大学生物医学技术中心）；Universidad Politécnica de Madrid（马德里理工大学）；ETS de Ingenieros Informáticos, Universidad Politécnica de Madrid（马德里理工大学计算机工程高等技术学院）
PACE-RAG: Patient-Aware Contextual and Evidence-Constrained RAG for Clinical Drug Recommendation
Korea Advanced Institute of Science and Technology（韩国科学技术院）；Department of Neurology, Seoul National University Hospital（首尔国立大学医院神经科）；Asan Medical Center, University of Ulsan, College of Medicine（釜山大学医学院阿桑医院）；Haeundae Paik Hospital, Inje University（庆尚大学医院海undae医院）
GraphMed-LT: Patient-Specific Graph Memory with Latent Clinical Thought Refinement for Multi-Turn Medical Conversations
School of Computing Science, University of Glasgow, UK（格拉斯哥大学计算机科学学院）；School of Natural and Computing Science, University of Aberdeen, UK（阿伯丁大学自然与计算科学学院）
Recommending Clinical Trials for Online Patient Cases using Artificial Intelligence
National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆）；National Cancer Institute, National Institutes of Health（美国国立卫生研究院国家癌症研究所）
Natural Language Processing with Deep Learning for Medical Adverse Event Detection from Free-Text Medical Narratives: A Case Study of Detecting Total Hip Replacement Dislocation
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Karolinska Institutet（卡罗林斯卡学院）；Sahlgrenska University Hospital（萨尔格伦斯卡大学医院）
Generative AI Is Not Ready for Clinical Use in Patient Education for Lower Back Pain Patients, Even With Retrieval-Augmented Generation
University of Pittsburgh（匹兹堡大学）；Stern School of Business, New York University（纽约大学斯特恩商学院）；New York University（纽约大学）；Alliance Physical Therapy（联合物理治疗公司）；Athletico Physical Therapy（Athletico物理治疗公司）；University of Pittsburgh Medical Center（匹兹堡大学医学中心）
