Advanced End-to-End Relation Extraction with Deep Neural Networks

使用深度神经网络进行高级端到端关系提取

• 批准号: 10615695
• 负责人: Venkata Naga Ramakanth Kavuluru
• 金额: $ 33.27万
• 依托单位: UNIVERSITY OF KENTUCKY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10615695
• 关键词: Adverse event; Architecture; Area; Benchmarking; Bioinformatics; Biology; Biomedical Research; Black race; Classification; Clinical; Code; Collaborations; Combination Drug Therapy; Communication; Communities; Complex; Computer software; Data; Data Set; Dependence; Disease; Distant; Drug Interactions; Encapsulated; Etiology; Evaluation; Fostering; Funding; Future; Generations; Genes; Growth; Hand; Heart; Information Retrieval; Information Sciences; Intramural Research; Joints; Knowledge Discovery; Label; Language; Lead; Link; Literature; Manuals; Maps; Measures; Methodology; Methods; Modeling; Molecular; Names; Natural Language Processing; Patients; Peer Review; Performance; Periodicals; Pharmaceutical Preparations; Physicians; Positioning Attribute; Process; Reporting; Research; Research Personnel; Resources; Review Literature; Scientist; Semantics; Software Tools; Source; Standardization; Structure; Supervision; System; Terminology; Testing; Text; Training; Translational Research; Trees; United States National Library of Medicine; biomedical data science; clinical care; deep neural network; improved; insight; interest; knowledge base; knowledgebase; machine learning method; natural language; neural; neural network; neural network architecture; new therapeutic target; novel; off-label use; protein protein interaction; side effect; social media; supervised learning; syntax; transfer learning

ABSTRACT Relations linking various biomedical entities constitute a crucial resource that enables biomedical data science applications and knowledge discovery. Relational information spans the translational science spectrum going from biology (e.g., protein–protein interactions) to translational bioinformatics (e.g., gene–disease associations), and eventually to clinical care (e.g., drug–drug interactions). Scientists report newly discovered relations in nat- ural language through peer-reviewed literature and physicians may communicate them in clinical notes. More recently, patients are also reporting side-effects and adverse events on social media. With exponential growth in textual data, advances in biomedical natural language processing (BioNLP) methods are gaining prominence for biomedical relation extraction (BRE) from text. Most current efforts in BRE follow a pipeline approach containing named entity recognition (NER), entity normalization (EN), and relation classification (RC) as subtasks. They typically suffer from error snowballing — errors in a component of the pipeline leading to more downstream errors — resulting in lower performance of the overall BRE system. This situation has lead to evaluation of different BRE substaks conducted in isolation. In this proposal we make a strong case for strictly end-to-end evaluations where relations are to be produced from raw text. We propose novel deep neural network architectures that model BRE in an end-to-end fashion and directly identify relations and corresponding entity spans in a single pass. We also extend our architectures to n-ary and cross-sentence settings where more than two entities may need to be linked even as the relation is expressed across multiple sentences. We also propose to create two new gold standard BRE datasets, one for drug–disease treatment relations and another first of a kind dataset for combination drug therapies. Our main hypothesis is that our end-to-end extraction models will yield supe- rior performance when compared with traditional pipelines. We test this through (1). intrinsic evaluations based on standard performance measures with several gold standard datasets and (2). extrinsic application oriented assessments of relations extracted with use-cases in information retrieval, question answering, and knowledge base completion. All software and data developed as part of this project will be made available for public use and we hope this will foster rigorous end-to-end benchmarking of BRE systems.

摘要

项目成果

Improved biomedical word embeddings in the transformer era.

• DOI: 10.1016/j.jbi.2021.103867
• 发表时间: 2021-08
• 期刊: Journal of biomedical informatics
• 影响因子: 4.5
• 作者: Noh J;Kavuluru R
• 通讯作者: Kavuluru R

Acquisition of a Lexicon for Family History Information: Bidirectional Encoder Representations From Transformers-Assisted Sublanguage Analysis.

获得家族史信息的词典获取：来自变形金刚辅助的串联分析的双向编码器表示。

• DOI: 10.2196/48072
• 发表时间: 2023-06-27
• 期刊: JMIR MEDICAL INFORMATICS
• 影响因子: 3.2
• 作者: Wang, Liwei;He, Huan;Wen, Andrew;Moon, Sungrim;Fu, Sunyang;Peterson, Kevin J.;Ai, Xuguang;Liu, Sijia;Kavuluru, Ramakanth;Liu, Hongfang
• 通讯作者: Liu, Hongfang

An open natural language processing (NLP) framework for EHR-based clinical research: a case demonstration using the National COVID Cohort Collaborative (N3C).

• DOI: 10.1093/jamia/ocad134
• 发表时间: 2023-11-17
• 期刊: JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
• 影响因子: 6.4
• 作者: Liu, Sijia;Wen, Andrew;Wang, Liwei;He, Huan;Fu, Sunyang;Miller, Robert;Williams, Andrew;Harris, Daniel;Kavuluru, Ramakanth;Liu, Mei;Abu-el-Rub, Noor;Schutte, Dalton;Zhang, Rui;Rouhizadeh, Masoud;Osborne, John D.;He, Yongqun;Topaloglu, Umit;Hong, Stephanie S.;Saltz, Joel H.;Schaffter, Thomas;Pfaff, Emily;Chute, Christopher G.;Duong, Tim;Haendel, Melissa A.;Fuentes, Rafael;Szolovits, Peter;Xu, Hua;Liu, Hongfang
• 通讯作者: Liu, Hongfang