Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Yuri, Ahuja"'
Autor:
Akhil Vaid, Joy Jiang, Ashwin Sawant, Stamatios Lerakis, Edgar Argulian, Yuri Ahuja, Joshua Lampert, Alexander Charney, Hayit Greenspan, Jagat Narula, Benjamin Glicksberg, Girish N Nadkarni
Publikováno v:
npj Digital Medicine, Vol 6, Iss 1, Pp 1-8 (2023)
Abstract The electrocardiogram (ECG) is a ubiquitous diagnostic modality. Convolutional neural networks (CNNs) applied towards ECG analysis require large sample sizes, and transfer learning approaches for biomedical problems may result in suboptimal
Externí odkaz:
https://doaj.org/article/91be2fa3a0f7494c9d9c088a378a693d
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-12 (2022)
Abstract While there exist numerous methods to identify binary phenotypes (i.e. COPD) using electronic health record (EHR) data, few exist to ascertain the timings of phenotype events (i.e. COPD onset or exacerbations). Estimating event times could e
Externí odkaz:
https://doaj.org/article/aa0e0f0810bd4787b84b2631eb4447d3
Autor:
Yuri Ahuja, Nicole Kim, Liang Liang, Tianrun Cai, Kumar Dahal, Thany Seyok, Chen Lin, Sean Finan, Katherine Liao, Guergana Savovoa, Tanuja Chitnis, Tianxi Cai, Zongqi Xia
Publikováno v:
Annals of Clinical and Translational Neurology, Vol 8, Iss 4, Pp 800-810 (2021)
Abstract Objective No relapse risk prediction tool is currently available to guide treatment selection for multiple sclerosis (MS). Leveraging electronic health record (EHR) data readily available at the point of care, we developed a clinical tool fo
Externí odkaz:
https://doaj.org/article/b8d3ddfae31444d8848d2b7bb21c45cb
Autor:
Yue Li, Pratheeksha Nair, Xing Han Lu, Zhi Wen, Yuening Wang, Amir Ardalan Kalantari Dehaghi, Yan Miao, Weiqi Liu, Tamas Ordog, Joanna M. Biernacka, Euijung Ryu, Janet E. Olson, Mark A. Frye, Aihua Liu, Liming Guo, Ariane Marelli, Yuri Ahuja, Jose Davila-Velderrain, Manolis Kellis
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-17 (2020)
Electronic Health Records (EHR) are subject to noise, biases and missing data. Here, the authors present MixEHR, a multi-view Bayesian framework related to collaborative filtering and latent topic models for EHR data integration and modeling.
Externí odkaz:
https://doaj.org/article/f93fdf7cc75646b5afb46727b4bdaa9e
Autor:
Tianrun Cai, Katherine P. Liao, Nicole Kim, Tanuja Chitnis, Yuri Ahuja, Thany Seyok, Zongqi Xia, Liang Liang, Kumar Dahal, Chen Lin, Tianxi Cai, Guergana Savovoa, Sean Finan
Publikováno v:
Annals of Clinical and Translational Neurology, Vol 8, Iss 4, Pp 800-810 (2021)
Annals of Clinical and Translational Neurology
Annals of Clinical and Translational Neurology
Objective No relapse risk prediction tool is currently available to guide treatment selection for multiple sclerosis (MS). Leveraging electronic health record (EHR) data readily available at the point of care, we developed a clinical tool for predict
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fa936ebab9ffd9d0b35a10eef099f06b
https://doaj.org/article/b8d3ddfae31444d8848d2b7bb21c45cb
https://doaj.org/article/b8d3ddfae31444d8848d2b7bb21c45cb
Autor:
Kumar Dahal, Daniel H. Solomon, Katherine P. Liao, Tianxi Cai, Zeling He, Katherine A. Yates, Yuri Ahuja, Houchen Lyu, Chang Xu, Sara K. Tedeschi, Tianrun Cai, Kazuki Yoshida, Chuan Hong
Publikováno v:
Arthritis Care Res (Hoboken)
OBJECTIVE Identifying pseudogout in large data sets is difficult due to its episodic nature and a lack of billing codes specific to this acute subtype of calcium pyrophosphate (CPP) deposition disease. The objective of this study was to evaluate a no
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::70f254db6c2dadacb26acadc405a18ed
https://doi.org/10.1002/acr.24132
https://doi.org/10.1002/acr.24132
Autor:
Jose Davila-Velderrain, Weiqi Liu, Liming Guo, Euijung Ryu, Zhi Wen, Yan Miao, Manolis Kellis, Janet E. Olson, Mark A. Frye, Yue Li, Ariane Marelli, Yuening Wang, Aihua Liu, Yuri Ahuja, Xing Han Lu, Tamas Ordog, Pratheeksha Nair, Joanna M. Biernacka, Amir Ardalan Kalantari Dehaghi
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-17 (2020)
Nature Communications
Nature Communications
Electronic health records (EHR) are rich heterogeneous collections of patient health information, whose broad adoption provides clinicians and researchers unprecedented opportunities for health informatics, disease-risk prediction, actionable clinica
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fdd1e96c3892a15d3a3f114aedba46f6
http://link.springer.com/article/10.1038/s41467-020-16378-3
http://link.springer.com/article/10.1038/s41467-020-16378-3
While there exist numerous methods to identify binary phenotypes (i.e. COPD) using electronic health record (EHR) data, few exist to ascertain the timings of phenotype events (i.e. COPD onset or exacerbations). Estimating event times could enable mor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d50028bb1b6a2c61b4bfc56215d9d5d0
https://doi.org/10.21203/rs.3.rs-1119858/v1
https://doi.org/10.21203/rs.3.rs-1119858/v1
Publikováno v:
Journal of Biomedical Informatics. 134:104190
Electronic Health Records (EHRs) contain rich clinical data collected at the point of the care, and their increasing adoption offers exciting opportunities for clinical informatics, disease risk prediction, and personalized treatment recommendation.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2b13d30e32c8a8b6a125b926d1f7d165
https://doi.org/10.1016/j.jbi.2022.104190
https://doi.org/10.1016/j.jbi.2022.104190
Publikováno v:
AMIA
ObjectiveWhile there exist numerous methods to predict binary phenotypes using electronic health record (EHR) data, few exist for prediction of phenotype event times, or equivalently phenotype state progression. Estimating such quantities could enabl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ff34e45cf5762d1c6062ae21f3c41b43
https://doi.org/10.1101/2021.03.07.21253096
https://doi.org/10.1101/2021.03.07.21253096