Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Bernardo Canovas-Segura"'
Autor:
Isidoro J. Casanova, Manuel Campos, Jose M. Juarez, Antonio Gomariz, Bernardo Canovas-Segura, Marta Lorente-Ros, Jose A. Lorente
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 24, Iss 1, Pp 1-15 (2024)
Abstract Background Pattern mining techniques are helpful tools when extracting new knowledge in real practice, but the overwhelming number of patterns is still a limiting factor in the health-care domain. Current efforts concerning the definition of
Externí odkaz:
https://doaj.org/article/0bb4aed6be9e43e9a9c83ee8ce79ecf9
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-11 (2023)
Abstract Validated and curated datasets are essential for studying the spread and control of infectious diseases in hospital settings, requiring clinical information on patients’ evolution and their location. The literature shows that approaches ba
Externí odkaz:
https://doaj.org/article/e77770c9f4354434855a63d6c8ae0b17
Publikováno v:
Algorithms, Vol 16, Iss 6, p 274 (2023)
Subgroup Discovery (SD) is a supervised data mining technique for identifying a set of relations (subgroups) among attributes from a dataset with respect to a target attribute. Two key components of this technique are (i) the metric used to quantify
Externí odkaz:
https://doaj.org/article/10ed86011a6542f7a84645a9e76d88fa
Publikováno v:
Distributed Computing and Artificial Intelligence, Special Sessions, 19th International Conference ISBN: 9783031232091
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fdaafbd7f81ffb5b231b5a92072ea8e9
https://doi.org/10.1007/978-3-031-23210-7_15
https://doi.org/10.1007/978-3-031-23210-7_15
Autor:
Francisco Javier Perales Palacios, Antonio Morales, Bernardo Canovas-Segura, Manuel Campos, Jose M. Juarez
Publikováno v:
Knowledge-Based Systems. 167:98-113
Background: The process of adding new knowledge in the form of rules to already running Clinical Decision Support Systems (CDSSs) in hospitals is extremely costly and time consuming. There are two principal limitations: (1) the lack of a broad consen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::64feb0ae022aa245204083730f9c7720
https://doi.org/10.1016/j.knosys.2019.01.007
https://doi.org/10.1016/j.knosys.2019.01.007
Publikováno v:
HEALTHINF
Scopus-Elsevier
Scopus-Elsevier
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d92862b39e1b80c1917fef6dc6821a1b
https://doi.org/10.5220/0010387207410746
https://doi.org/10.5220/0010387207410746
Publikováno v:
Artificial Intelligence in Medicine ISBN: 9783030772109
AIME
AIME
Seasonality plays a significant role in the prevalence of infectious diseases. We evaluate the performance of different approaches used to deal with seasonality in clinical prediction models, including a new proposal based on sliding windows. Class i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f4f86ad33c76c4289b6c5d18a4dce53f
https://doi.org/10.1007/978-3-030-77211-6_16
https://doi.org/10.1007/978-3-030-77211-6_16
Publikováno v:
Artificial Intelligence in Medicine ISBN: 9783030772109
AIME
AIME
The phenotyping process consists of selecting sets of patients of special interest and identifying their key characteristics. Subgroup Discovery (SD) is a suitable supervised approach for this task. In this work, we have proposed a two step process w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ae7879165e32f2148b7a7a64249803ac
https://doi.org/10.1007/978-3-030-77211-6_27
https://doi.org/10.1007/978-3-030-77211-6_27
Publikováno v:
Bioinformatics and Biomedical Engineering ISBN: 9783030453848
IWBBIO
IWBBIO
Infections acquired in healthcare settings (nosocomial infections) have become one of the main health problems in acute care centers. Some of epidemiologists’ efforts are focused on studying patient’s traceability and determining the main factors
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::50f109f3b378f1aaa8f77523d7dfc634
https://doi.org/10.1007/978-3-030-45385-5_9
https://doi.org/10.1007/978-3-030-45385-5_9
Publikováno v:
Knowledge-Based Systems. 232:107469
Background: The current situation of critical progression as regards the resistance of bacteria to antibiotics has led to the use of machine learning techniques in order to provide clinicians with new knowledge for decision making. One of the key asp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::82f0bb37e7a91a681829c66eed9cc142
https://doi.org/10.1016/j.knosys.2021.107469
https://doi.org/10.1016/j.knosys.2021.107469