Zobrazeno 1 - 10
of 799
pro vyhledávání: '"Prosperi, Mattia"'
Publikováno v:
JMIR Public Health and Surveillance, Vol 6, Iss 4, p e24661 (2020)
Externí odkaz:
https://doaj.org/article/487aa84da0ce49fba78c8ec08c061ca0
Publikováno v:
JMIR Public Health and Surveillance, Vol 6, Iss 3, p e22853 (2020)
Externí odkaz:
https://doaj.org/article/ff16abb68f58488ebc50ac2f4655aa1e
Publikováno v:
JMIR Public Health and Surveillance, Vol 6, Iss 2, p e19170 (2020)
BackgroundThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has been growing exponentially, affecting over 4 million people and causing enormous distress to economies and societies worldwide. A plethora of analyses based on vi
Externí odkaz:
https://doaj.org/article/a70bfce838f5473f9694279e2dad748f
Autor:
Chen, Aokun, Paredes, Daniel, Yu, Zehao, Lou, Xiwei, Brunson, Roberta, Thomas, Jamie N., Martinez, Kimberly A., Lucero, Robert J., Magoc, Tanja, Solberg, Laurence M., Snigurska, Urszula A., Ser, Sarah E., Prosperi, Mattia, Bian, Jiang, Bjarnadottir, Ragnhildur I., Wu, Yonghui
Delirium is an acute decline or fluctuation in attention, awareness, or other cognitive function that can lead to serious adverse outcomes. Despite the severe outcomes, delirium is frequently unrecognized and uncoded in patients' electronic health re
Externí odkaz:
http://arxiv.org/abs/2304.00111
Publikováno v:
Journal="AMIA Annu Symp Proc", Year="2022", Volume="2022",Pages="485--494"
Determining causal effects of interventions onto outcomes from real-world, observational (non-randomized) data, e.g., treatment repurposing using electronic health records, is challenging due to underlying bias. Causal deep learning has improved over
Externí odkaz:
http://arxiv.org/abs/2303.04201
Estimating treatment effects, especially individualized treatment effects (ITE), using observational data is challenging due to the complex situations of confounding bias. Existing approaches for estimating treatment effects from longitudinal observa
Externí odkaz:
http://arxiv.org/abs/2207.11251
Autor:
Jun, Inyoung, Marini, Simone, Boucher, Christina A., Morris, J. Glenn, Bian, Jiang, Prosperi, Mattia
Bacterial infections are responsible for high mortality worldwide. Antimicrobial resistance underlying the infection, and multifaceted patient's clinical status can hamper the correct choice of antibiotic treatment. Randomized clinical trials provide
Externí odkaz:
http://arxiv.org/abs/2207.07458
Whole genome sequencing (WGS) is quickly becoming the customary means for identification of antimicrobial resistance (AMR) due to its ability to obtain high resolution information about the genes and mechanisms that are causing resistance and driving
Externí odkaz:
http://arxiv.org/abs/2107.03383
Social media, especially Twitter, is being increasingly used for research with predictive analytics. In social media studies, natural language processing (NLP) techniques are used in conjunction with expert-based, manual and qualitative analyses. How
Externí odkaz:
http://arxiv.org/abs/2003.12139
Autor:
Zhang, Hansi, Wheldon, Christopher, Dunn, Adam G., Tao, Cui, Huo, Jinhai, Zhang, Rui, Prosperi, Mattia, Guo, Yi, Bian, Jiang
Objectives To test the feasibility of using Twitter data to assess determinants of consumers' health behavior towards Human papillomavirus (HPV) vaccination informed by the Integrated Behavior Model (IBM). Methods We used three Twitter datasets spann
Externí odkaz:
http://arxiv.org/abs/1907.11624