Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Luwan Zhang"'
Autor:
Tianrun Cai, Luwan Zhang, Nicole Yang, Kanako K. Kumamaru, Frank J. Rybicki, Tianxi Cai, Katherine P. Liao
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 19, Iss 1, Pp 1-7 (2019)
Abstract Background Electronic medical records (EMR) contain numerical data important for clinical outcomes research, such as vital signs and cardiac ejection fractions (EF), which tend to be embedded in narrative clinical notes. In current practice,
Externí odkaz:
https://doaj.org/article/3e93da8db427472f917d983fc2a7277b
Autor:
Nathan Palmer, David Y. Zhang, Chuan Hong, Luwan Zhang, Paul Avillach, Samuel G. Finlayson, Isaac S. Kohane
Polygenic risk scores (PRS) bear great promise in understanding complex diseases and improving clinical diagnoses, but the competency of these risk scores in different populations is known to vary significantly, especially between those of European a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bc5d7b64775c82187defaec91a5f7752
Autor:
Kanako K. Kumamaru, Frank J. Rybicki, Nicole Yang, Tianxi Cai, Luwan Zhang, Tianrun Cai, Katherine P. Liao
Publikováno v:
BMC Medical Informatics and Decision Making
BMC Medical Informatics and Decision Making, Vol 19, Iss 1, Pp 1-7 (2019)
BMC Medical Informatics and Decision Making, Vol 19, Iss 1, Pp 1-7 (2019)
Background Electronic medical records (EMR) contain numerical data important for clinical outcomes research, such as vital signs and cardiac ejection fractions (EF), which tend to be embedded in narrative clinical notes. In current practice, this dat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::33d846d0b5c18e3ea843ab7f9d1fbf2a
https://pubmed.ncbi.nlm.nih.gov/31730484
https://pubmed.ncbi.nlm.nih.gov/31730484
Autor:
Yuri Ahuja, Tianrun Cai, Joshua C. Denny, Robert J. Carroll, Luwan Zhang, Isaac S. Kohane, Yuk-Lam Ho, Andrew L. Beam, Katherine P. Liao, Yichi Zhang, Kelly Cho, Tianxi Cai, Zeling He
Publikováno v:
J Biomed Inform
Objective With its increasingly widespread adoption, electronic health records (EHR) have enabled phenotypic information extraction at an unprecedented granularity and scale. However, often a medical concept (e.g. diagnosis, prescription, symptom) is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f984dd38d400717166d749074313c1bd
https://doi.org/10.1016/j.jbi.2019.103322
https://doi.org/10.1016/j.jbi.2019.103322
Summary Although recovering a Euclidean distance matrix from noisy observations is a common problem in practice, how well this could be done remains largely unknown. To fill in this void, we study a simple distance matrix estimate based on the so-cal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1f96e15a9c05131df3c3dd5b0c34dfcb
http://arxiv.org/abs/1409.5009
http://arxiv.org/abs/1409.5009