Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Rave Harpaz"'
Publikováno v:
PLoS ONE, Vol 7, Iss 7, p e41471 (2012)
Adverse drug events (ADEs) detection and assessment is at the center of pharmacovigilance. Data mining of systems, such as FDA's Adverse Event Reporting System (AERS) and more recently, Electronic Health Records (EHRs), can aid in the automatic detec
Externí odkaz:
https://doaj.org/article/5bfc109cce1e4168bac515b9e4ecb81b
Autor:
Rave, Harpaz, William, DuMouchel, Robbert, Van Manen, Alexander, Nip, Steve, Bright, Ana, Szarfman, Joseph, Tonning, Magnus, Lerch
Publikováno v:
Drug Safety. 45:765-780
Statistical signal detection is a crucial tool for rapidly identifying potential risks associated with pharmaceutical products. The unprecedented environment created by the coronavirus disease 2019 (COVID-19) pandemic for vaccine surveillance predisp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::18faf5fc017eeeb46367bdc69174bf42
https://doi.org/10.1007/s40264-022-01186-z
https://doi.org/10.1007/s40264-022-01186-z
Publikováno v:
Communications in Computer and Information Science ISBN: 9783030937324
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9bc24eed98099df31af24d2f52b899cd
https://doi.org/10.1007/978-3-030-93733-1_37
https://doi.org/10.1007/978-3-030-93733-1_37
Autor:
Pushpraj Shukla, Sheng Wang, William DuMouchel, Rave Harpaz, Walter Sun, Ryen W. White, Eric Horvitz, Apurv Pant
Publikováno v:
Journal of Biomedical Informatics. 59:42-48
Display Omitted Study use of search logs for the early detection of known adverse drug reactions (ADRs).Employ a time-indexed reference standard as ground truth in our prospective analysis.Propose novel methods to control for the advance availability
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dd1b8fde4c2626560e29f4cf42b00675
https://doi.org/10.1016/j.jbi.2015.11.005
https://doi.org/10.1016/j.jbi.2015.11.005
Autor:
Nigam H. Shah, Ryen W. White, William DuMouchel, Anna Ripple, Carol Friedman, Martijn J. Schuemie, Rainer Winnenburg, Alfred Sorbello, Eric Horvitz, Olivier Bodenreider, Rave Harpaz
Publikováno v:
J Biomed Inform
Display Omitted Improving signal detection is key to strengthening drug safety surveillance.Multimodal signal detection (MSD) is based on jointly analyzing multiple data sources.This manuscript broadens evaluations of MSD done in prior studies.The re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ada9340d4d564e15050af21738e279a9
https://pubmed.ncbi.nlm.nih.gov/29081385
https://pubmed.ncbi.nlm.nih.gov/29081385
Publikováno v:
Journal of the American Medical Informatics Association : JAMIA, vol 21, iss 2
Background and objective Electronic health records (EHRs) are increasingly being used to complement the FDA Adverse Event Reporting System (FAERS) and to enable active pharmacovigilance. Over 30% of all adverse drug reactions are caused by drug–dru
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::74ee0aa6840a522566aea33ddb6c7e3c
https://doi.org/10.1136/amiajnl-2013-001612
https://doi.org/10.1136/amiajnl-2013-001612
Publikováno v:
Clinical Pharmacology & Therapeutics. 99:268-270
Big Data holds the promise of fundamentally transforming the manner in which adverse drug reactions can be identified and evaluated. This commentary discusses new data sources that are envisioned to form a Big Data-enabled pharmacovigilance system an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::182178bee56514add0d442a98a63a58e
https://doi.org/10.1002/cpt.302
https://doi.org/10.1002/cpt.302
Publikováno v:
Journal of the American Medical Informatics Association. 19:1066-1074
Background Drug–drug interactions (DDIs) are responsible for many serious adverse events; their detection is crucial for patient safety but is very challenging. Currently, the US Food and Drug Administration and pharmaceutical companies are showing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::294981412b903d6448d3a3d9502e3a5f
https://doi.org/10.1136/amiajnl-2012-000935
https://doi.org/10.1136/amiajnl-2012-000935
Autor:
Patrick B. Ryan, William DuMouchel, Nigam H. Shah, Carol Friedman, David Madigan, Rave Harpaz
Publikováno v:
Clinical Pharmacology & Therapeutics. 91:1010-1021
An important goal of the health system is to identify new adverse drug events (ADEs) in the postapproval period. Datamining methods that can transform data into meaningful knowledge to inform patient safety have proven essential for this purpose. New
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::32ffc52fab8628810e2fa9b78ba1756d
https://doi.org/10.1038/clpt.2012.50
https://doi.org/10.1038/clpt.2012.50
Autor:
Rave Harpaz, Hector R. Perez, George Hripcsak, Herbert S. Chase, Carol Friedman, Raul Rabadan
Publikováno v:
Clinical Pharmacology & Therapeutics. 89:243-250
In this article, we present a new pharmacovigilance data mining technique based on the biclustering paradigm, which is designed to identify drug groups that share a common set of adverse events (AEs) in the spontaneous reporting system (SRS) of the U
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cc2907eb7fc27af5b2c407fcd69cc609
https://doi.org/10.1038/clpt.2010.285
https://doi.org/10.1038/clpt.2010.285