Challenges in Natural Language Processing in Clinical Text
Challenges in Natural Language Processing in Clinical Text
批准号:
9597333
负责人:
Ozlem Uzuner
金额:
$2.0万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-11-08 至 2018-11-07
中文摘要
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英文摘要
Challenges in Natural Language Processing for Clinical Narratives
Abstract: Narratives of electronic health records (EHRs) contain useful information that is difficult to
automatically extract, index, search, or interpret. Clinical natural language processing (NLP) technologies for
automatic extraction, indexing, searching, and interpretation of EHRs are in development; however, due to
privacy concerns related to EHRs, such technologies are usually developed by teams that have privileged
access to EHRs in a specific institution. Technologies that are tailored to a specific set of data from a given
institution generate inspiring results on that data; however, they can fail to generalize to similar data from other
institutions and even other departments from the same institution. Therefore, learning from these technologies
and building on them becomes difficult.
In order to improve NLP in EHRs, there is need for head-to-head comparison of approaches that can
address a given task on the same data set. Shared-tasks provide one way of conducting systematic head-to-
head comparisons. This proposal describes a series of shared-task challenges and conferences, spread over
a five year period, that promote the development and evaluation of cutting edge clinical NLP systems by
distributing de-identified EHRs to the broad research community, under data use agreements, so that:
the state-of-the-art in clinical NLP technologies can be identified and advanced,
a set of technologies that enable the use of the information contained in EHR narratives becomes
available, and
the information from EHR narratives can be made more accessible, for example, for clinical and
medical research.
The scientific activities supporting the organization of the shared-task challenges are sponsored in part by
Informatics for Integrating Biology and the Bedside (i2b2), grant number U54-LM008748, PI: Kohane. CEGS
NGRID, NIH P50 MH106933, PI: Isaac Kohane also supported these shared tasks.
This proposal aims to organize a series of workshops, conference proceedings, and journal special issues
that will accompany the shared-task challenges in order to disseminate the knowledge generated by the
challenges.
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DOI:
10.1016/j.jbi.2015.09.006
发表时间:
2015-12
期刊:
Journal of biomedical informatics
影响因子:
4.5
作者:
[Yang H, Garibaldi JM]
通讯作者:
Garibaldi JM
Mining heart disease risk factors in clinical text with named entity recognition and distributional semantic models.
使用命名实体识别和分布式语义模型挖掘临床文本中的心脏病风险因素。
DOI:
10.1016/j.jbi.2015.08.009
发表时间:
2015-12
期刊:
Journal of biomedical informatics
影响因子:
4.5
作者:
[Urbain J]
通讯作者:
Urbain J
DOI:
10.1016/j.jbi.2015.06.010
发表时间:
2015-12
期刊:
Journal of biomedical informatics
影响因子:
4.5
作者:
[Roberts K, Shooshan SE, Rodriguez L, Abhyankar S, Kilicoglu H, Demner-Fushman D]
通讯作者:
Demner-Fushman D
DOI:
10.1016/j.jbi.2015.06.024
发表时间:
2015-12
期刊:
Journal of biomedical informatics
影响因子:
4.5
作者:
[Solomon JW, Nielsen RD]
通讯作者:
Nielsen RD
DOI:
10.1016/j.jbi.2017.05.015
发表时间:
2017-11
期刊:
Journal of biomedical informatics
影响因子:
4.5
作者:
[Liu Y, Gu Y, Nguyen JC, Li H, Zhang J, Gao Y, Huang Y]
通讯作者:
Huang Y
共 27 条
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National NLP Clinical Challenges (n2c2): Challenges in Natural Language Processing for Clinical Narratives
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依托单位:
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海外基金
Natural超对称中的希格斯物理与暗物质研究
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负责人:郑思波
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Natural超对称在LHC上的现象学研究
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