Zobrazeno 1 - 10
of 82
pro vyhledávání: '"Wehner, Gregory J."'
Autor:
Ulloa, Alvaro, Jing, Linyuan, Good, Christopher W, vanMaanen, David P, Raghunath, Sushravya, Suever, Jonathan D, Nevius, Christopher D, Wehner, Gregory J, Hartzel, Dustin, Leader, Joseph B, Alsaid, Amro, Patel, Aalpen A, Kirchner, H Lester, Pattichis, Marios S, Haggerty, Christopher M, Fornwalt, Brandon K
Predicting future clinical events helps physicians guide appropriate intervention. Machine learning has tremendous promise to assist physicians with predictions based on the discovery of complex patterns from historical data, such as large, longitudi
Externí odkaz:
http://arxiv.org/abs/1811.10553
Autor:
Ulloa, Alvaro, Basile, Anna, Wehner, Gregory J., Jing, Linyuan, Ritchie, Marylyn D., Beaulieu-Jones, Brett, Haggerty, Christopher M., Fornwalt, Brandon K.
Electronic health records (EHR) contain a large variety of information on the clinical history of patients such as vital signs, demographics, diagnostic codes and imaging data. The enormous potential for discovery in this rich dataset is hampered by
Externí odkaz:
http://arxiv.org/abs/1801.00065
Autor:
Wehner, Gregory J., Suever, Jonathan D., Fielden, Samuel W., Powell, David K., Hamlet, Sean M., Vandsburger, Moriel H., Haggerty, Christopher M., Zhong, Xiaodong, Fornwalt, Brandon K.
Publikováno v:
In Magnetic Resonance Imaging December 2018 54:90-100
Autor:
Wehner, Gregory J.
Publikováno v:
Theses and Dissertations--Biomedical Engineering.
Recent evidence suggests that cardiac mechanics (e.g. cardiac strains) are better measures of heart function compared to common clinical metrics like ejection fraction. However, commonly-used parameters of cardiac mechanics remain limited to just a f
Externí odkaz:
http://uknowledge.uky.edu/cbme_etds/48
http://uknowledge.uky.edu/cgi/viewcontent.cgi?article=1051&context=cbme_etds
http://uknowledge.uky.edu/cgi/viewcontent.cgi?article=1051&context=cbme_etds
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Autor:
Samad, Manar D, Ulloa, Alvaro, Wehner, Gregory J, Linyuan, Jing, Hartzel, Dustin, Good, Christopher W, Williams, Brent A., Haggerty, Christopher M, Fornwalt, Brandon K
OBJECTIVES: Use machine learning to more accurately predict survival after echocardiography. BACKGROUND: Predicting patient outcomes (e.g. survival) following echocardiography is primarily based on ejection fraction (EF) and comorbidities. However, t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::d69c39d23f95bbf9b67679aee262f432
https://europepmc.org/articles/PMC6286869/
https://europepmc.org/articles/PMC6286869/
Autor:
Ulloa, Alvaro, Basile, Anna, Wehner, Gregory J., Jing, Linyuan, Ritchie, Marylyn D., Beaulieu-Jones, Brett, Haggerty, Christopher M., Fornwalt, Brandon K.
Electronic health records (EHR) contain a large variety of information on the clinical history of patients such as vital signs, demographics, diagnostic codes and imaging data. The enormous potential for discovery in this rich dataset is hampered by
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fd9f80e34009829d0deae7c5b62291f8
Akademický článek
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Autor:
Wehner, Gregory J.
Recent evidence suggests that cardiac mechanics (e.g. cardiac strains) are better measures of heart function compared to common clinical metrics like ejection fraction. However, commonly-used parameters of cardiac mechanics remain limited to just a f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1e1753cddb076b960afa7b13eb55496d