Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Yingcheng Sun"'
Autor:
Tian Kang, Yingcheng Sun, Jae Hyun Kim, Casey Ta, Adler Perotte, Kayla Schiffer, Mutong Wu, Yang Zhao, Nour Moustafa-Fahmy, Yifan Peng, Chunhua Weng
Publikováno v:
Journal of the American Medical Informatics Association. 30:1022-1031
Objective To develop a computable representation for medical evidence and to contribute a gold standard dataset of annotated randomized controlled trial (RCT) abstracts, along with a natural language processing (NLP) pipeline for transforming free-te
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::08f688efe0f8a7c17fd15bb0701ae3c3
https://doi.org/10.1093/jamia/ocad036
https://doi.org/10.1093/jamia/ocad036
Autor:
Jae Hyun Kim, Yingcheng Sun, Chunhua Weng, Alex Butler, Casey N. Ta, Hao Liu, James R. Rogers, Ibrahim Diallo
Publikováno v:
Appl Clin Inform
Background Clinical trials are the gold standard for generating robust medical evidence, but clinical trial results often raise generalizability concerns, which can be attributed to the lack of population representativeness. The electronic health rec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a85e4bfc2ad6aff196630642a671c979
https://doi.org/10.1055/s-0041-1733846
https://doi.org/10.1055/s-0041-1733846
Publikováno v:
Studies in health technology and informatics. 290
The rapid growth of clinical trials launched in recent years poses significant challenges for accurate and efficient trial search. Keyword-based clinical trial search engines require users to construct effective queries, which can be a difficult task
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::4a59e291b55dac0d49811e83a0568531
https://pubmed.ncbi.nlm.nih.gov/35673024
https://pubmed.ncbi.nlm.nih.gov/35673024
The rapid growth of clinical trials launched in recent years poses significant challenges for accurate and efficient trial search. Keyword-based clinical trial search engines require users to construct effective queries, which can be a difficult task
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c2fb1afde2f9158dd0f73a96c7cec7bf
https://doi.org/10.3233/shti220085
https://doi.org/10.3233/shti220085
Publikováno v:
International Journal of Semantic Computing. 14:273-293
With the explosive growth of online discussions published everyday on social media platforms, comprehension and discovery of the most popular topics have become a challenging problem. Conventional topic models have had limited success in online discu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c1d16915d436bed5455c5826de1bf42d
https://doi.org/10.1142/s1793351x20400103
https://doi.org/10.1142/s1793351x20400103
Autor:
Yilu Fang, Betina Idnay, Yingcheng Sun, Hao Liu, Zhehuan Chen, Karen Marder, Hua Xu, Rebecca Schnall, Chunhua Weng
Publikováno v:
Journal of the American Medical Informatics Association : JAMIA. 29(7)
Objective To combine machine efficiency and human intelligence for converting complex clinical trial eligibility criteria text into cohort queries. Materials and Methods Criteria2Query (C2Q) 2.0 was developed to enable real-time user intervention for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3efde4affc7c81d736fe61e7bdfbdc57
https://pubmed.ncbi.nlm.nih.gov/35426943
https://pubmed.ncbi.nlm.nih.gov/35426943
Autor:
Junghwan Lee, Cong Liu, Junyoung Kim, Zhehuan Chen, Yingcheng Sun, James R. Rogers, Wendy K. Chung, Chunhua Weng
Publikováno v:
Journal of Biomedical Informatics. 135:104227
Although individually rare, collectively more than 7,000 rare diseases affect about 10% of patients. Each of the rare diseases impacts the quality of life for patients and their families, and incurs significant societal costs. The low prevalence of e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3e63165c8fff0db09d2ad1795b77a85f
https://doi.org/10.1016/j.jbi.2022.104227
https://doi.org/10.1016/j.jbi.2022.104227
Autor:
Yilu, Fang, Jae Hyun, Kim, Betina Ross, Idnay, Rebeca, Aragon Garcia, Carmen E, Castillo, Yingcheng, Sun, Hao, Liu, Cong, Liu, Chi, Yuan, Chunhua, Weng
Publikováno v:
Studies in health technology and informatics. 281
Clinical trial eligibility criteria are important for selecting the right participants for clinical trials. However, they are often complex and not computable. This paper presents the participatory design of a human-computer collaboration method for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::d5a26522d2ddb1a057b34c479346248f
https://pubmed.ncbi.nlm.nih.gov/34042820
https://pubmed.ncbi.nlm.nih.gov/34042820
Autor:
Betina Ross S. Idnay, Chunhua Weng, Yingcheng Sun, Rebeca Aragon Garcia, Jae Hyun Kim, Cong Liu, Chi Yuan, Carmen Castillo, Hao Liu, Yilu Fang
Publikováno v:
MIE
Clinical trial eligibility criteria are important for selecting the right participants for clinical trials. However, they are often complex and not computable. This paper presents the participatory design of a human-computer collaboration method for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::14eabb96babc6c6358d91fc5c9ecec33
https://doi.org/10.3233/shti210325
https://doi.org/10.3233/shti210325
Autor:
Chunhua Weng, Chi Yuan, Christopher T. Southard, Hao Liu, Yingcheng Sun, Latoya A. Stewart, Jae Hyun Kim, Alex Butler
Publikováno v:
Journal of Biomedical Informatics
Graphical abstract
Clinical trials are essential for generating reliable medical evidence, but often suffer from expensive and delayed patient recruitment because the unstructured eligibility criteria description prevents automatic query generat
Clinical trials are essential for generating reliable medical evidence, but often suffer from expensive and delayed patient recruitment because the unstructured eligibility criteria description prevents automatic query generat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::15def6404776cf32da8ea0d6f25c6498
https://pubmed.ncbi.nlm.nih.gov/33887457
https://pubmed.ncbi.nlm.nih.gov/33887457