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.
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）
Recent rapid increase in the generation of clinical data and rapid development of computational science make us able to extract new insights from massive datasets in healthcare industry. Oncological clinical notes are creating rich databases for documenting patients history and they potentially contain lots of patterns that could help in better management of the disease. However, these patterns are locked within free text (unstructured) portions of clinical documents and consequence in limiting health professionals to extract useful information from them and to finally perform Query and Answer…
Kings College London（伦敦国王学院）；Kings College Hospital NHS Foundation Trust（国王学院医院国民保健服务基金会信托）
The data available in Electronic Health Records (EHRs) provides the opportunity to transform care, and the best way to provide better care for one patient is through learning from the data available on all other patients. Temporal modelling of a patient's medical history, which takes into account the sequence of past events, can be used to predict future events such as a diagnosis of a new disorder or complication of a previous or existing disorder. While most prediction approaches use mostly the structured data in EHRs or a subset of single-domain predictions and outcomes, we present MedGPT a…
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）
Northwestern University Feinberg School of Medicine（西北大学范伯格医学院）
To reduce the large amount of time spent screening, identifying, and recruiting patients into clinical trials, we need prescreening systems that are able to automate the data extraction and decision-making tasks that are typically relegated to clinical research study coordinators. However, a major obstacle is the vast amount of patient data available as unstructured free-form text in electronic health records. Here we propose an information extraction-based approach that first automatically converts unstructured text into a structured form. The structured data are then compared against a list …
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）
Preventable medical errors are estimated to be among the leading causes of injury and death in the United States. To prevent such errors, healthcare systems have implemented patient safety and incident reporting systems. These systems enable clinicians to report unsafe conditions and cases where patients have been harmed due to errors in medical care. These reports are narratives in natural language and while they provide detailed information about the situation, it is non-trivial to perform large scale analysis for identifying common causes of errors and harm to the patients. In this work, we…
Medical errors are leading causes of death in the US and as such, prevention of these errors is paramount to promoting health care. Patient Safety Event reports are narratives describing potential adverse events to the patients and are important in identifying and preventing medical errors. We present a neural network architecture for identifying the type of safety events which is the first step in understanding these narratives. Our proposed model is based on a soft neural attention model to improve the effectiveness of encoding long sequences. Empirical results on two large-scale real-world …
Postsurgical complications (PSCs) are known as a deviation from the normal postsurgical course and categorized by severity and treatment requirements. Surgical site infection (SSI) is one of major PSCs and the most common healthcare-associated infection, resulting in increased length of hospital stay and cost. In this work, we assessed an automated way to generate lexicon (i.e., keyword features) from clinical narratives using sublanguage analysis with heuristics to detect SSI and evaluated these keywords with medical experts. To further validate our approach, we also conducted decision tree a…
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）
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）
Accurate and timely detection of medical adverse events (AEs) from free-text medical narratives is challenging. Natural language processing (NLP) with deep learning has already shown great potential for analyzing free-text data, but its application for medical AE detection has been limited. In this study we proposed deep learning based NLP (DL-NLP) models for efficient and accurate hip dislocation AE detection following total hip replacement from standard (radiology notes) and non-standard (follow-up telephone notes) free-text medical narratives. We benchmarked these proposed models with a wid…
Background: More than half (57%) of pharma clinical research spend is in support of clinical trials. One reason is that Electronic Health Record (EHR) systems and HIPAA privacy rules often limit how broadly patient information can be shared, resulting in laborious human efforts to manually collect, de-identify, and summarize patient information for use in clinical studies. Purpose: Conduct feasibility study for a Rheumatoid Arthritis (RA) clinical trial in an Accountable Care Organization. Measure prevalence of RA and related conditions matching study criteria. Evaluate automation of patient d…
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）
