Zobrazeno 1 - 10
of 72
pro vyhledávání: '"Valerio Maggio"'
Publikováno v:
Journal of Medical Internet Research, Vol 25, p e42734 (2023)
BackgroundThe use of social media data to predict mental health outcomes has the potential to allow for the continuous monitoring of mental health and well-being and provide timely information that can supplement traditional clinical assessments. How
Externí odkaz:
https://doaj.org/article/add2ec4c86e845fab381f34e2b1e1342
Autor:
Nina H Di Cara, Jiao Song, Valerio Maggio, Christopher Moreno-Stokoe, Alastair R Tanner, Benjamin Woolf, Oliver S P Davis, Alisha Davies
Publikováno v:
International Journal of Population Data Science, Vol 5, Iss 4 (2021)
Background Disasters such as the COVID-19 pandemic pose an overwhelming demand on resources that cannot always be met by official organisations. Limited resources and human response to crises can lead members of local communities to turn to one anoth
Externí odkaz:
https://doaj.org/article/6c63deac18614f8ba9fa55bece82c4a0
Autor:
Diego Fioravanti, Ylenia Giarratano, Valerio Maggio, Claudio Agostinelli, Marco Chierici, Giuseppe Jurman, Cesare Furlanello
Publikováno v:
BMC Bioinformatics, Vol 19, Iss S2, Pp 1-13 (2018)
Abstract Background Convolutional Neural Networks can be effectively used only when data are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of pixels in images. We introduce here Ph-CNN, a novel deep learning ar
Externí odkaz:
https://doaj.org/article/e149ec0cf6a94911b07e470a41a03c5c
Autor:
Alastair R Tanner, Nina H Di Cara, Valerio Maggio, Richard Thomas, Andy Boyd, Luke Sloan, Tarek Al Baghal, John Macleod, Claire M A Haworth, Oliver S P Davis
Publikováno v:
Tanner, A, Di Cara, N H, Maggio, V, Thomas, R G, Boyd, A, Sloan, L, Al Baghal, T, Macleod, J A A, Haworth, C M A & Davis, O S P 2023, ' Epicosm — a framework for linking online social media in epidemiological cohorts ', International Journal of Epidemiology, vol. 52, no. 3, dyad020, pp. 952-957 . https://doi.org/10.1093/ije/dyad020
MotivationSocial media represent an unrivalled opportunity for epidemiological cohorts to collect large amounts of high-resolution time course data on mental health. Equally, the high-quality data held by epidemiological cohorts could greatly benefit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0f3da9aa314555b2f28004780aa6a673
https://hdl.handle.net/1983/b82301d2-7726-45a2-8a11-fe010759d1e3
https://hdl.handle.net/1983/b82301d2-7726-45a2-8a11-fe010759d1e3
Autor:
Andrea Bizzego, Nicole Bussola, Marco Chierici, Valerio Maggio, Margherita Francescatto, Luca Cima, Marco Cristoforetti, Giuseppe Jurman, Cesare Furlanello
Publikováno v:
PLoS Computational Biology, Vol 15, Iss 3, p e1006269 (2019)
Artificial Intelligence is exponentially increasing its impact on healthcare. As deep learning is mastering computer vision tasks, its application to digital pathology is natural, with the promise of aiding in routine reporting and standardizing resu
Externí odkaz:
https://doaj.org/article/d188b729581a43bf83e90ee7e8c7b81c
Publikováno v:
PLoS ONE, Vol 13, Iss 12, p e0208924 (2018)
We introduce the CDRP (Concatenated Diagnostic-Relapse Prognostic) architecture for multi-task deep learning that incorporates a clinical algorithm, e.g., a risk stratification schema to improve prognostic profiling. We present the first application
Externí odkaz:
https://doaj.org/article/712d730ee0074de9a6eb75e2436cde6c
BACKGROUND The use of social media data to predict mental health outcomes has the potential to allow for the continuous monitoring of mental health and well-being and provide timely information that can supplement traditional clinical assessments. Ho
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d479c8b0a808d726fd7198f969452529
https://doi.org/10.2196/preprints.42734
https://doi.org/10.2196/preprints.42734
Background : The use of social media data in predicting mental health outcomes has the potential to allow for continuous monitoring of mental health and well-being, and to provide timely information that can supplement traditional clinical assessment
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::da45dba56927c2c7af446bcb2f2a3fa4
https://doi.org/10.31219/osf.io/4sne7
https://doi.org/10.31219/osf.io/4sne7
Autor:
Nina H. Di Cara, Natalie Zelenka, Huw Day, Euan D. S. Bennet, Vanessa Hanschke, Valerio Maggio, Ola Michalec, Charles Radclyffe, Roman Shkunov, Emma Tonkin, Zoë Turner, Kamilla Wells
Publikováno v:
Di Cara, N, Zelenka, N R, Day, H, Bennet, E D S, Hanschke, V A, Maggio, V, Michalec, O, Radclyffe, C J D, Shkunov, R, Tonkin, E L, Turner, Z & Wells, K 2022, ' Data Ethics Club : Creating a collaborative space to discuss data ethics ', Patterns, vol. 3, no. 7, 100537 . https://doi.org/10.1016/j.patter.2022.100537
Awareness and management of ethical issues in data science is becoming increasingly relevant to us all, and a crucial skill for data scientists. Discussion of contemporary issues in collaborative and interdisciplinary spaces is an engaging way to all
Autor:
Nina H Di Cara, Valerio Maggio, Benjamin Woolf, Christopher Moreno-Stokoe, Alisha R Davies, Jiao Song, Alastair R. Tanner, Oliver S. P. Davis
Publikováno v:
Di Cara, N, Song, J, Maggio, V, Moreno-Stokoe, C, Tanner, A, Woolf, B, Davis, O S P & Davies, A 2021, ' Mapping Population Vulnerability and Community Support during COVID-19 : a case study from Wales ', International Journal of Population Data Science, vol. 5, no. 4, 1409 . https://doi.org/10.23889/IJPDS.V5I4.1409
International Journal of Population Data Science
International Journal of Population Data Science
Background: Disasters such as the COVID-19 pandemic pose an overwhelming demand on resources that cannot always be met by official organisations. Limited resources and human response to crises can lead members of local communities to turn to one anot
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::305dbbf5eed6edc369153ae6a4fbd356
https://hdl.handle.net/1983/5be27813-833a-45fd-adc6-d71c87de2cdd
https://hdl.handle.net/1983/5be27813-833a-45fd-adc6-d71c87de2cdd