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
- 来源:
- 关键词:BlindedCOVID-19 pandemicCharacteristicsClinicalComplexComprehensionConsumptionDatabasesEnsureEvaluationFaceGeneral PopulationGenerationsHealthHealth ProfessionalHealth behaviorHealthcareHumanKnowledgeLanguageLearningLiteratureManualsMechanicsMedicalMethodsModelingNatural Language ProcessingParticipantPatient RecruitmentsPeer ReviewPerformanceProceduresReadabilityReaderReadingResearchSourceStructureSystemTextTrainingTranslatingTranslationsTreatment outcomeVocabularyWorkWritingcomputer generateddeep learningdeep learning modelhealth literacyimprovedimproved outcomeknowledge baseliteracymachine translationmembermulti-task learningneuralneural modelneural networknoveloptimal treatmentsstemsystematic reviewtransfer learning
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.
卫生知识普及是做出明智的卫生决策以改善结果的关键。然而,而同行——
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)
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Trevor Cohen其他文献
Trevor Cohen的其他文献
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{{ 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:
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