Professional to Plain Language Neural Translation: A Path Toward Actionable Health Information

专业到通俗语言的神经翻译:通向可行健康信息的道路

基本信息

  • 批准号:
    10579898
  • 负责人:
  • 金额:
    $ 21.16万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2022
  • 资助国家:
    美国
  • 起止时间:
    2022-03-01 至 2025-02-28
  • 项目状态:
    未结题

项目摘要

Health literacy is key to making well-informed health decisions that improve outcomes. However, while the peer- reviewed clinical literature contains valuable information to guide health decisions, it is generally written for an audience of healthcare professionals. Even in the context of good general literacy, medical jargon and the complex structure of professional language make this information especially hard to interpret. While efforts have been made to summarize some of this literature in plain language to make it accessible to the general public, these efforts depend on human expertise. This approach cannot scale to match the rapid pace at which new findings emerge in the literature. Thus, there is an urgent unmet need for automated methods to enhance the accessibility of the canonical biomedical literature to the general public. This problem can be framed as a type of translation problem, between the language of healthcare professionals, and that of healthcare consumers. The proposed research builds on recent advances in deep learning stemming from neural sequence- to-sequence models, which were originally evaluated in machine translation tasks. In our recent work, we showed these models can be effectively adapted to the task of translating between abstracts in the Cochrane Database of Systematic Reviews (CDSR) and corresponding professionally-authored plain language summaries. The resulting automatically-generated summaries outperformed those from other models in their alignment with professionally-authored summaries. Furthermore, in a pilot user evaluation in which participants were blinded as to summary provenance, they were generally judged favorably to their expert-authored counterparts. In the proposed research we will develop this line of research further, by evaluating the utility of additional pre-training and auxiliary fine-tuning tasks as a means to improve the quality of generated summaries. We will also customize the models concerned to enhance their factual accuracy and readability using novel auxiliary training objectives and post-processing procedures. We will evaluate our methods as compared with robust baseline models in system-centric evaluations of content alignment with reference summaries, readability and factual correctness. Using Mechanical Turk, we will conduct user-centric evaluations of the ease with which summaries from best-performing models can be understood, as compared with CDSR expert-authored plain language summaries. These evaluations will consider both perceived interpretability, and actual comprehension, with the latter evaluated using sets of multiple choice questions to probe comprehension, recall and learning. In doing so, the proposed research will advance the state-of-the-art in automated simplification and summarization of the biomedical literature for consumption by the general public.
健康素养是做出明智的健康决策以改善结果的关键。然而,虽然同行- 综述的临床文献包含指导健康决策的有价值的信息,通常是为 医疗保健专业人士的观众。即使在良好的一般文化背景下,医学术语和 专业语言的复杂结构使这些信息特别难以解释。尽管工作 我已经做了一些总结,这些文献在平原语言,使其访问的一般 公共,这些努力依赖于人类的专业知识。这种方法无法扩展到匹配的快速步伐, 新的发现出现在文献中。因此,迫切需要自动化的方法来增强 一般公众可获得规范的生物医学文献。这个问题可以被定义为 医疗保健专业人员的语言与医疗保健专业人员的语言之间的翻译问题 消费者拟议的研究建立在神经序列深度学习的最新进展基础上, 序列模型,最初在机器翻译任务中进行评估。在最近的工作中,我们 表明这些模型可以有效地适用于科克伦中摘要之间的翻译任务 系统性综述数据库(CDSR)和相应的专业撰写的简明语言 总结。由此产生的自动生成的摘要在其 与专业撰写的摘要保持一致。此外,在一次试点用户评价中, 不知道摘要出处,他们通常被认为是他们的专家撰写的。 同行在拟议的研究中,我们将进一步发展这一研究路线,通过评估 额外的预训练和辅助微调任务,作为提高生成摘要质量的一种手段。 我们亦会采用新颖的模式,为有关模型作出特别设计,以提高其事实准确性和可读性。 辅助培训目标和后处理程序。我们将评估我们的方法, 在以系统为中心的内容评估中, 和事实的正确性使用Mechanical Turk,我们将进行以用户为中心的评估, 与CDSR专家撰写的普通摘要相比,性能最佳模型的摘要可以理解 语言摘要。这些评估将考虑感知的可解释性和实际的理解, 后者使用多项选择题来评估,以探索理解,回忆和学习。在 这样做,所提出的研究将推进自动简化和摘要的最新技术 生物医学文献供公众消费。

项目成果

期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Trevor Cohen其他文献

Trevor Cohen的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Trevor Cohen', 18)}}的其他基金

DeconDTN: Deconfounding Deep Transformer Networks for Clinical NLP
DeconDTN:为临床 NLP 解构深度 Transformer 网络
  • 批准号:
    10626888
  • 财政年份:
    2022
  • 资助金额:
    $ 21.16万
  • 项目类别:
Professional to Plain Language Neural Translation: A Path Toward Actionable Health Information
专业到通俗语言的神经翻译:通向可行健康信息的道路
  • 批准号:
    10349319
  • 财政年份:
    2022
  • 资助金额:
    $ 21.16万
  • 项目类别:
DeconDTN: Deconfounding Deep Transformer Networks for Clinical NLP
DeconDTN:为临床 NLP 解构深度 Transformer 网络
  • 批准号:
    10467107
  • 财政年份:
    2022
  • 资助金额:
    $ 21.16万
  • 项目类别:
DeconDTN: Deconfounding Deep Transformer Networks for Clinical NLP
DeconDTN:为临床 NLP 解构深度 Transformer 网络
  • 批准号:
    10711315
  • 财政年份:
    2022
  • 资助金额:
    $ 21.16万
  • 项目类别:
Computerized assessment of linguistic indicators of lucidity in Alzheimer's Disease dementia
阿尔茨海默病痴呆症语言清醒度指标的计算机化评估
  • 批准号:
    10093304
  • 财政年份:
    2020
  • 资助金额:
    $ 21.16万
  • 项目类别:
Using Biomedical Knowledge to Identify Plausible Signals for Pharmacovigilance
利用生物医学知识识别药物警戒的合理信号
  • 批准号:
    8914098
  • 财政年份:
    2013
  • 资助金额:
    $ 21.16万
  • 项目类别:
Using Biomedical Knowledge to Identify Plausible Signals for Pharmacovigilance
利用生物医学知识识别药物警戒的合理信号
  • 批准号:
    8727094
  • 财政年份:
    2013
  • 资助金额:
    $ 21.16万
  • 项目类别:
Encoding Semantic Knowledge in Vector Space for Biomedical Information
在生物医学信息的向量空间中编码语义知识
  • 批准号:
    8138564
  • 财政年份:
    2010
  • 资助金额:
    $ 21.16万
  • 项目类别:
Encoding Semantic Knowledge in Vector Space for Biomedical Information
在生物医学信息的向量空间中编码语义知识
  • 批准号:
    7977263
  • 财政年份:
    2010
  • 资助金额:
    $ 21.16万
  • 项目类别:

相似海外基金

RAPID: Affective Mechanisms of Adjustment in Diverse Emerging Adult Student Communities Before, During, and Beyond the COVID-19 Pandemic
RAPID:COVID-19 大流行之前、期间和之后不同新兴成人学生社区的情感调整机制
  • 批准号:
    2402691
  • 财政年份:
    2024
  • 资助金额:
    $ 21.16万
  • 项目类别:
    Standard Grant
Depopulating Holding Centers during the COVID-19 Pandemic
COVID-19 大流行期间收容中心的人口减少
  • 批准号:
    2413624
  • 财政年份:
    2024
  • 资助金额:
    $ 21.16万
  • 项目类别:
    Standard Grant
Return to work after the COVID-19 pandemic: a biopsychosocial perspective
COVID-19 大流行后重返工作岗位:生物心理社会视角
  • 批准号:
    24K16416
  • 财政年份:
    2024
  • 资助金额:
    $ 21.16万
  • 项目类别:
    Grant-in-Aid for Early-Career Scientists
Leveraging 'Positive Deviance' to improve learning under COVID-19 pandemic. A randomized intervention in rural East Africa
利用“正偏差”改善 COVID-19 大流行期间的学习。
  • 批准号:
    23K20687
  • 财政年份:
    2024
  • 资助金额:
    $ 21.16万
  • 项目类别:
    Grant-in-Aid for Scientific Research (B)
Leveraging an Existing Longitudinal Observational Cohort to Understand the Impacts of Cannabis Legalization and the COVID-19 Pandemic on Alcohol and Cannabis Use in At-risk Young Adults
利用现有的纵向观察队列来了解大麻合法化和 COVID-19 大流行对高危年轻人酒精和大麻使用的影响
  • 批准号:
    478397
  • 财政年份:
    2023
  • 资助金额:
    $ 21.16万
  • 项目类别:
    Operating Grants
Genomic Epidemiology of Methicillin-Resistant Staphylococcus aureus Infections Prior to and During the COVID-19 Pandemic
COVID-19 大流行之前和期间耐甲氧西林金黄色葡萄球菌感染的基因组流行病学
  • 批准号:
    494305
  • 财政年份:
    2023
  • 资助金额:
    $ 21.16万
  • 项目类别:
    Operating Grants
Fact-finding survey of nursing diagnoses, nursing outcomes, and nursing interventions in medical institution and edcational institution under COVID-19 pandemic
COVID-19大流行背景下医疗机构和教育机构护理诊断、护理结果和护理干预的实况调查
  • 批准号:
    23K09822
  • 财政年份:
    2023
  • 资助金额:
    $ 21.16万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
Communities in Crises: The Dynamics of Social Resources for Resilience and Recovery in the Wake of the COVID-19 Pandemic
危机中的社区:COVID-19 大流行后社会资源的弹性和恢复动态
  • 批准号:
    ES/W00349X/1
  • 财政年份:
    2023
  • 资助金额:
    $ 21.16万
  • 项目类别:
    Research Grant
Impact of Different Learning Modalities on Science and Mathematics Teachers' Effectiveness and Retention during the COVID-19 Pandemic
COVID-19 大流行期间不同学习方式对科学和数学教师的有效性和保留率的影响
  • 批准号:
    2243392
  • 财政年份:
    2023
  • 资助金额:
    $ 21.16万
  • 项目类别:
    Continuing Grant
Did business support measures during COVID-19 pandemic create Zombie firms?: Evidence from Post-pandemic corporate performance
COVID-19 大流行期间的企业支持措施是否创造了僵尸公司?:大流行后企业绩效的证据
  • 批准号:
    23K18808
  • 财政年份:
    2023
  • 资助金额:
    $ 21.16万
  • 项目类别:
    Grant-in-Aid for Research Activity Start-up
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了