Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Julia, Hoxha"'
Publikováno v:
Humanities & Social Sciences Communications, Vol 8, Iss 1, Pp 1-10 (2021)
Abstract The expansion of research on migration over recent decades has neglected sending and transit countries. Whether in terms of their internal development, their diaspora policy, their shift from primarily sending countries to (potential) transi
Externí odkaz:
https://doaj.org/article/ac2af18c948746449b0df35d6c119f05
Autor:
Julia Hoxha, Eneida A. Mendonça, Gregory William Hruby, Praveen Chandar Ravichandran, David A. Hanauer, Chunhua Weng
Publikováno v:
International Journal of Medical Informatics. 91:1-9
Objectives The Patient, Intervention, Control/Comparison, and Outcome (PICO) framework is an effective technique for framing a clinical question. We aim to develop the counterpart of PICO to structure clinical research data needs. Methods We use a da
Autor:
Julia Hoxha, Chunhua Weng
Publikováno v:
Journal of Biomedical Informatics. 61:176-184
The worldwide adoption of electronic health records (EHR) promises to accelerate clinical research, which lies at the heart of medical advances. However, the interrogation of such Big Data by clinical researchers can be laborious and error-prone, inv
Publikováno v:
Journal of Biomedical Informatics. 59:89-101
Clinical data access involves complex but opaque communication between medical researchers and query analysts. Understanding such communication is indispensable for designing intelligent human–machine dialog systems that automate query formulation.
Autor:
Maria Rosalba Demartis, Paolo, Contini, Cataldi, Silvia, Gennaro, Iorio, Annamaria, Campanini, Elisabetta, Neve, Luigi, Minerba, Raffaella, Rubino, Fabrizio, Gentile, Katiuscia, Carnà, Bialakowsky, Mariano, Sasín, Tomás, Nougués, Manuel, Zapico, Julieta, Barrero, Agustín, Bertelli, Elisa, Ichaso, Pellegrini, Giuseppe, Auriemma, Vincenzo, Milton, Petruczok, Silvia, Serafini, Magdalena, Biolik, Ján, Morovič, Francesca, Cisternino, Rudina, Rama, Irida, Agolli, Julia, Hoxha, Aleksandra, Kłos-Skrzypczak, Lucas Tavares Galindo Filho, Isabella, Quatera, Concetta, Papapicco, Piotr, Andrukiewicz, Achilova, Altynai, Arkadiusz, Wuwer, María Clara Desalvo, Edlira Ngjeci Shima, Irida Agolli Nasufi, Anxhela, Bruçi, Enrico, Orizio, Gerlamo, Spreafico, Consuelo Huerta Olivares, Danae Videla Igor, Ana Inés Frere Affanni, Beatrice, Gnudi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3666::92253a0c8d1a13356e063f124f14ccff
http://hdl.handle.net/11386/4721394
http://hdl.handle.net/11386/4721394
In this paper, we present an automated method for taxonomy learning, focusing on concept formation and hierarchical relation learning. To infer such relations, we partition the extracted concepts and group them into closely-related clusters using Hie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::87ab10613bc9a4c5f9535590e305a57c
https://europepmc.org/articles/PMC5077645/
https://europepmc.org/articles/PMC5077645/
Display Omitted We extend GIST for quantifying population representativeness of related studies.We contribute a method for iteratively characterizing underrepresented patients.We demonstrated the value of this method using Type 2 diabetes mellitus st
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::74739c416d064c568227c29ec958820e
https://escholarship.org/uc/item/26g7n4kf
https://escholarship.org/uc/item/26g7n4kf
Publikováno v:
Fundamentals of Service Systems ISBN: 9783319231945
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::21514ab24aa289511a4d087dee7d7448
https://doi.org/10.1007/978-3-319-23195-2_6
https://doi.org/10.1007/978-3-319-23195-2_6
Autor:
Julia Hoxha, Achim Rettinger
Publikováno v:
ICMLA (2)
In this paper, we address the task of inferring user preference relationships about various objects in order to generate relevant recommendations. The majority of the traditional approaches to the problem assume a flat representation of the data, and
Publikováno v:
ICMLA (2)
Most traditional recommender systems focus on the objective of improving the accuracy of recommendations in a single domain. However, preferences of users may extend over multiple domains, especially in the Web where users often have browsing prefere