Zobrazeno 1 - 10
of 207
pro vyhledávání: '"Shen, Feichen"'
Autor:
Shen, Feichen, Liu, Sijia, Fu, Sunyang, Wang, Yanshan, Henry, Sam, Uzuner, Ozlem, Liu, Hongfang
Publikováno v:
JMIR Medical Informatics, Vol 9, Iss 1, p e24008 (2021)
BackgroundAs a risk factor for many diseases, family history (FH) captures both shared genetic variations and living environments among family members. Though there are several systems focusing on FH extraction using natural language processing (NLP)
Externí odkaz:
https://doaj.org/article/f57c1abdb0594bb0a6b258e4d7d2d82c
Publikováno v:
JMIR Medical Informatics, Vol 8, Iss 11, p e23375 (2020)
BackgroundSemantic textual similarity is a common task in the general English domain to assess the degree to which the underlying semantics of 2 text segments are equivalent to each other. Clinical Semantic Textual Similarity (ClinicalSTS) is the sem
Externí odkaz:
https://doaj.org/article/ce4011b55db64cbcb2aaa546ea9a83b8
Autor:
Liu, Sijia, Wang, Yanshan, Wen, Andrew, Wang, Liwei, Hong, Na, Shen, Feichen, Bedrick, Steven, Hersh, William, Liu, Hongfang
Publikováno v:
JMIR Medical Informatics, Vol 8, Iss 10, p e17376 (2020)
BackgroundWidespread adoption of electronic health records has enabled the secondary use of electronic health record data for clinical research and health care delivery. Natural language processing techniques have shown promise in their capability to
Externí odkaz:
https://doaj.org/article/b27ca7a8d0f1412f8ec53bd0fb435a8a
Cancer is responsible for millions of deaths worldwide every year. Although significant progress has been achieved in cancer medicine, many issues remain to be addressed for improving cancer therapy. Appropriate cancer patient stratification is the p
Externí odkaz:
http://arxiv.org/abs/2103.16316
Cancer is responsible for millions of deaths worldwide every year. Although significant progress hasbeen achieved in cancer medicine, many issues remain to be addressed for improving cancer therapy.Appropriate cancer patient stratification is the pre
Externí odkaz:
http://arxiv.org/abs/2101.05866
Autor:
Fu, Sunyang, Chen, David, He, Huan, Liu, Sijia, Moon, Sungrim, Peterson, Kevin J, Shen, Feichen, Wang, Liwei, Wang, Yanshan, Wen, Andrew, Zhao, Yiqing, Sohn, Sunghwan, Liu, Hongfang
Publikováno v:
Journal of Biomedical Informatics (2020): 103526
Background Concept extraction, a subdomain of natural language processing (NLP) with a focus on extracting concepts of interest, has been adopted to computationally extract clinical information from text for a wide range of applications ranging from
Externí odkaz:
http://arxiv.org/abs/1910.11377
Autor:
Shen, Feichen
In recent years, the size of big linked data has grown rapidly and this number is still rising. Big linked data and knowledge bases come from different domains such as life sciences, publications, media, social web, and so on. However, with the rapid
Externí odkaz:
http://arxiv.org/abs/1902.07688
Autor:
Liu, Sijia, Wang, Yanshan, Wen, Andrew, Wang, Liwei, Hong, Na, Shen, Feichen, Bedrick, Steven, Hersh, William, Liu, Hongfang
Background: Widespread adoption of electronic health records (EHRs) has enabled secondary use of EHR data for clinical research and healthcare delivery. Natural language processing (NLP) techniques have shown promise in their capability to extract th
Externí odkaz:
http://arxiv.org/abs/1901.07601
Autor:
Wang, Yanshan, Afzal, Naveed, Fu, Sunyang, Wang, Liwei, Shen, Feichen, Rastegar-Mojarad, Majid, Liu, Hongfang
The wide adoption of electronic health records (EHRs) has enabled a wide range of applications leveraging EHR data. However, the meaningful use of EHR data largely depends on our ability to efficiently extract and consolidate information embedded in
Externí odkaz:
http://arxiv.org/abs/1808.09397
Autor:
Wang, Yanshan, Sohn, Sunghwan, Liu, Sijia, Shen, Feichen, Wang, Liwei, Atkinson, Elizabeth J., Amin, Shreyasee, Liu, Hongfang
Objective: To automatically create large labeled training datasets and reduce the efforts of feature engineering for training accurate machine learning models for clinical information extraction. Materials and Methods: We propose a distant supervisio
Externí odkaz:
http://arxiv.org/abs/1804.07814