Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/42963
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Castiglione, Filippo | - |
dc.contributor.author | Daugulis, Peteris | - |
dc.contributor.author | MANCINI, Emiliano | - |
dc.contributor.author | Oldenkamp, Rik | - |
dc.contributor.author | Schultsz, Constance | - |
dc.contributor.author | Vagale, Vija | - |
dc.date.accessioned | 2024-05-15T11:30:02Z | - |
dc.date.available | 2024-05-15T11:30:02Z | - |
dc.date.issued | 2024 | - |
dc.date.submitted | 2024-05-15T09:31:20Z | - |
dc.identifier.citation | Baltic Journal of Modern Computing, 12 (1) , p. 30 -49 | - |
dc.identifier.uri | http://hdl.handle.net/1942/42963 | - |
dc.description.abstract | Antimicrobial resistance prediction is a pivotal ongoing research activity that is currently being explored across various levels. In this context, we present the application of two prediction methods that model the antimicrobial resistance of Neisseria gonorrhoeae on the national level as an outcome of socio-economic processes. The methods use two different implementations of the principal component analysis combined with classification algorithms. Using these two methods, we generated forecasts concerning antimicrobial resistance of Neisseria gonorrhoeae, using publicly available databases encompassing over 200 countries from 1998 to 2021. Both approaches exhibit similar mean absolute averages and correlations when comparing available measurements with predictions. Steps of statistical analysis and applications are and error reduction possibilities. | - |
dc.description.sponsorship | Help and consultations were provided by Antonio Cappuccio. The authors acknowledge partial funding from the following national funding agencies participating in the project MAGIcIAN JPI-AMR (https://www.magician-amr.eu/): Latvian Council of Science (LZP, Latvia) and the Italian Ministry of Education and Research (MIUR, Italy). | - |
dc.language.iso | en | - |
dc.publisher | UNIV LATVIA | - |
dc.subject.other | PCA | - |
dc.subject.other | principal component regression | - |
dc.subject.other | antimicrobial resistance | - |
dc.subject.other | AMR prevalence prediction | - |
dc.subject.other | Neisseria gonorrhoea | - |
dc.subject.other | surveillance | - |
dc.title | Predicting Antimicrobial Resistance Trends Combining Standard Linear Algebra with Machine Learning Algorithms | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 49 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 30 | - |
dc.identifier.volume | 12 | - |
local.format.pages | 20 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | Daugulis, P (corresponding author), Daugavpils Univ, Daugavpils, Latvia. | - |
dc.description.notes | filippo.castiglione@tii.ae; peteris.daugulis@du.lv; | - |
dc.description.notes | emiliano.mancini@uhasselt.be; r.oldenkamp@vu.nl; c.schultsz@aighd.org; | - |
dc.description.notes | vija.vagale@du.lv | - |
local.publisher.place | RAINA BULVARIS 19, RIGA, LV-1586, LATVIA | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.identifier.doi | 10.22364/bjmc.2024.12.1.03 | - |
dc.identifier.isi | 001194961500007 | - |
local.provider.type | wosris | - |
local.description.affiliation | [Castiglione, Filippo] Technol Innovat Inst, Biotechnol Res Ctr, POB 9639, Abu Dhabi, U Arab Emirates. | - |
local.description.affiliation | [Daugulis, Peteris; Vagale, Vija] Daugavpils Univ, Daugavpils, Latvia. | - |
local.description.affiliation | [Mancini, Emiliano] Hasselt Univ, Data Sci Inst, Hasselt, Belgium. | - |
local.description.affiliation | [Mancini, Emiliano; Schultsz, Constance] Univ Amsterdam, Amsterdam UMC, Dept Global Hlth, Amsterdam, Netherlands. | - |
local.description.affiliation | [Oldenkamp, Rik] Vrije Univ Amsterdam, Amsterdam Inst Life & Environm, Dept Environm & Hlth, Amsterdam, Netherlands. | - |
local.uhasselt.international | yes | - |
item.fulltext | With Fulltext | - |
item.contributor | Castiglione, Filippo | - |
item.contributor | Daugulis, Peteris | - |
item.contributor | MANCINI, Emiliano | - |
item.contributor | Oldenkamp, Rik | - |
item.contributor | Schultsz, Constance | - |
item.contributor | Vagale, Vija | - |
item.accessRights | Restricted Access | - |
item.fullcitation | Castiglione, Filippo; Daugulis, Peteris; MANCINI, Emiliano; Oldenkamp, Rik; Schultsz, Constance & Vagale, Vija (2024) Predicting Antimicrobial Resistance Trends Combining Standard Linear Algebra with Machine Learning Algorithms. In: Baltic Journal of Modern Computing, 12 (1) , p. 30 -49. | - |
crisitem.journal.issn | 2255-8942 | - |
crisitem.journal.eissn | 2255-8950 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Baltic Journal of Modern Computing, 2013, Vol.1, No.1.pdf Restricted Access | Published version | 819.04 kB | Adobe PDF | View/Open Request a copy |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.