Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Premanand Tiwari"'
Autor:
Rachel Kim, Krithika Suresh, Michael A. Rosenberg, Malinda S. Tan, Daniel C. Malone, Larry A. Allen, David P. Kao, Heather D. Anderson, Premanand Tiwari, Katy E. Trinkley
Publikováno v:
Frontiers in Cardiovascular Medicine, Vol 10 (2023)
Introduction/backgroundPatients with heart failure and reduced ejection fraction (HFrEF) are consistently underprescribed guideline-directed medications. Although many barriers to prescribing are known, identification of these barriers has relied on
Externí odkaz:
https://doaj.org/article/91269e04017d47ccb6279af93e4ce1d7
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 20, Iss 1, Pp 1-10 (2020)
Abstract Background With cardiovascular disease increasing, substantial research has focused on the development of prediction tools. We compare deep learning and machine learning models to a baseline logistic regression using only ‘known’ risk fa
Externí odkaz:
https://doaj.org/article/1540d2ffa1bb456d96a15251d5f11046
Publikováno v:
J Cardiovasc Pharmacol Ther
Background: Drug-induced QT prolongation is a potentially preventable cause of morbidity and mortality, however there are no widespread clinical tools utilized to predict which individuals are at greatest risk. Machine learning (ML) algorithms may pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::34cf6865e77a0a8d52e42adb31a896b0
https://pubmed.ncbi.nlm.nih.gov/33682475
https://pubmed.ncbi.nlm.nih.gov/33682475
Supplemental Material, sj-docx-1-cpt-10.1177_1074248421995348 for Prediction of Drug-Induced Long QT Syndrome Using Machine Learning Applied to Harmonized Electronic Health Record Data by Steven T. Simon, Divneet Mandair, Premanand Tiwari and Michael
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::18d90b9d1883dfecf384d00b9b26fe66
Publikováno v:
BMC Medical Informatics and Decision Making
BMC Medical Informatics and Decision Making, Vol 20, Iss 1, Pp 1-10 (2020)
BMC Medical Informatics and Decision Making, Vol 20, Iss 1, Pp 1-10 (2020)
Background With cardiovascular disease increasing, substantial research has focused on the development of prediction tools. We compare deep learning and machine learning models to a baseline logistic regression using only ‘known’ risk factors in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::38a6019df6ddf24597a958da4bfd7736
http://europepmc.org/articles/PMC7532582
http://europepmc.org/articles/PMC7532582
Autor:
Debashis Ghosh, Michael A. Rosenberg, Derek E. Smith, Premanand Tiwari, Kathryn L. Colborn, Fuyong Xing
Publikováno v:
JAMA Network Open
Key Points Question Can machine learning approaches applied to harmonized electronic health record data identify patients at risk of 6-month incident atrial fibrillation with greater accuracy than standard risk factors? Findings This diagnostic study
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cbd3a555f285e632e8c40f4d3a5e23e8
http://europepmc.org/articles/PMC6991266
http://europepmc.org/articles/PMC6991266
Additional file 1 Python code used for analysis. Supplemental Figure 1. Calibration curve for optimal model with results from re-scaling methods
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1ac3cb44a16b68ec902cdb3571aef202
Publikováno v:
Journal of the American College of Cardiology. 75:194
With the burden of cardiovascular disease expected to increase considerably, substantial research has focused on the development of prediction tools to assess who might develop disease. Machine learning offers a means to mobilize large amounts of EHR
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c956916c22f44fc270b5cab68cf232ae
https://doi.org/10.1016/s0735-1097(20)30821-4
https://doi.org/10.1016/s0735-1097(20)30821-4