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Transfer Learning for Digital Curation of the EMR Clinical Narrative

用于 EMR 临床叙述数字化管理的迁移学习

基本信息

批准号：
10647748
负责人：
GUERGANA K. SAVOVA
金额：
$ 37.61万
依托单位：
BOSTON CHILDREN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-12 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10647748
关键词：
Adverse event Apache Appearance Area Artificial Intelligence Automated Annotation Biomedical Research Brain Aneurysms Classification Clinical Clinical Investigator Communities Computer Vision Systems Computerized Medical Record Coupled Creativeness Data Data Science Data Set Databases Development Disease Engineering Epigenetic Process Evaluation Event Explosion Genetic Health Hepatotoxicity Image Information Retrieval Intuition Investigation Label Language Learning Machine Learning Medicine Methodology Methods Methotrexate Modeling Multiple Sclerosis Natural Language Processing Neural Network Simulation Patients Performance Pharmaceutical Preparations Phenotype Procedures Public Health Publications Publishing Rare Diseases Reporting Research Resources Rheumatoid Arthritis Running Semantics Severities Source Speech Structure Symptoms System Techniques Testing Text Time Training Translational Research Vision Work autism spectrum disorder base comorbidity computing resources deep learning deep neural network digital imaging Segmentation improved informatics tool learning strategy machine learning method multitask neural neural network next generation novel open source phenotypic data response support vector machine transfer learning unstructured data

项目摘要

Project Summary This proposal is in response to PAR 18-796 to seek support for advancing methodologies for a transfer learning framework for the digital curation of the Electronic Medical Records (EMR) clinical narrative. In the current era of increasing importance of Artificial Intelligence (AI) in biomedicine, our proposal tackles a critical AI component – automated annotation of health-related text. Since 2015 the development and application of machine learning (ML) methods has exploded propelled by the convergence of plentiful digitized unstructured data (text, speech, images), hardware and the refinement of neural networks or deep learning. 2018 marked a turning point in Natural Language Processing (NLP), particularly transfer learning through pre-trained models like Universal Language Model Fine-tuning for Text Classification, Allen AI's ELMO, OpenAI's Open-GPT. In November 2018, Google published the Bidirectional Encodings Representations from Transformers (BERT), a transformer-based model pre-trained on massive general text databases (3.3B words total). The publication reported using BERT representations to build classifiers for 11 NLP tasks which outperformed the state-of-the- art (SOTA) with large margins. The NLP research community jumped to the idea of exploring this new framework but quickly came to the realization that building BERT-style models from scratch is affordable and feasible to only a few. Thus, research investigation proceeded in the direction of using these gigantic models as resources for language representations. Scientific efforts focused on pre-trained models (e.g. BERT) as a source of extracting high quality language features or fine-tuning on a specific task, i.e. using a model as a checkpoint and re-training with much smaller amounts of task-specific data to produce predictions by typically adding one fully-connected layer on top of the representations and training for a few epochs. This general watershed shift in NLP to transfer learning which parallels the developments in computer vision a few years ago coupled with our latest work brings to the forefront a critical NLP research topic ripe for exploration – a transfer learning framework for the digital curation of the EMR clinical narrative. The proposed work is research of novel scientific methods for extracting detailed information from health-related text especially the EMR, the major source of phenotype data for patients. Precise phenotype information is needed to advance translational research, particularly to unravel the effects of genetic, epigenetic, and systems changes on responsiveness. This research is in line with the latest developments in neural deep learning approaches and AI in general and is expected to enhance biomedical research and through that the health of the public.

项目摘要本提案是对PAR 18-796的回应，旨在寻求支持，电子病历（EMR）临床叙述数字化管理的学习框架。在当前人工智能（AI）在生物医学中的重要性日益增加，我们的建议解决了一个关键问题， AI组件-健康相关文本的自动注释。自2015年以来，机器学习（ML）方法在大量数字化非结构化数据（文本、语音、图像）、硬件和神经网络或深度学习的改进。2018年，自然语言处理（NLP）的转折点，特别是通过预训练模型的迁移学习比如用于文本分类的通用语言模型微调，艾伦AI的埃尔莫，OpenAI的Open-GPT。在 2018年11月，谷歌发布了变形金刚双向编码表示（BERT），基于transformer的模型在大规模的通用文本数据库（总共33亿字）上进行了预训练。出版报告使用BERT表示来构建11个NLP任务的分类器，这些任务的性能超过了艺术（SOTA）与大利润率。NLP研究社区跳到探索这个新的想法，框架，但很快就意识到，从头开始构建BERT风格的模型是负担得起的，只对少数人可行。因此，研究调查朝着使用这些巨大模型的方向进行作为语言表征的资源。科学工作集中在预先训练的模型（例如BERT）上，提取高质量语言特征或对特定任务进行微调的来源，即使用模型作为检查点和重新训练，使用更少量的特定于任务的数据来生成预测，在表示的顶部添加一个完全连接的层，并训练几个时期。这个一般 NLP向迁移学习的分水岭式转变，这与计算机视觉的发展相似前加上我们的最新工作带来了前沿的一个关键的NLP研究课题成熟的探索-一个迁移学习框架，用于EMR临床叙述的数字化管理。拟议的工作是研究从健康相关文本中提取详细信息的新科学方法，特别是EMR，患者表型数据的主要来源。需要精确的表型信息来推进翻译研究，特别是解开遗传，表观遗传和系统变化对反应的影响。这项研究符合神经深度学习方法和人工智能的最新发展，预计将加强生物医学研究，并通过公众的健康。