| | SLO | ENG | Piškotki in zasebnost

Večja pisava | Manjša pisava
Prva stran > Izpis gradiva

Izpis gradiva Pomoč

Naslov:Agile Machine Learning Model Development Using Data Canyons in Medicine : A Step towards Explainable Artificial Intelligence and Flexible Expert-Based Model Improvement
Avtorji:ID Žlahtič, Bojan (Avtor)
ID Završnik, Jernej (Avtor)
ID Blažun Vošner, Helena (Avtor)
ID Kokol, Peter (Avtor)
ID Šuran, David (Avtor)
ID Završnik, Tadej (Avtor)
Datoteke:.pdf Zlahtic-2023-Agile_Machine_Learning_Model_Deve.pdf (5,28 MB)
MD5: 97D6E8BCC8104EE31B189778BAA2D394
 
URL https://www.mdpi.com/2076-3417/13/14/8329
 
Jezik:Angleški jezik
Vrsta gradiva:Znanstveno delo
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:FERI - Fakulteta za elektrotehniko, računalništvo in informatiko
FNM - Fakulteta za naravoslovje in matematiko
Opis:Over the past few decades, machine learning has emerged as a valuable tool in the field of medicine, driven by the accumulation of vast amounts of medical data and the imperative to harness this data for the betterment of humanity. However, many of the prevailing machine learning algorithms in use today are characterized as black-box models, lacking transparency in their decision-making processes and are often devoid of clear visualization capabilities. The transparency of these machine learning models impedes medical experts from effectively leveraging them due to the high-stakes nature of their decisions. Consequently, the need for explainable artificial intelligence (XAI) that aims to address the demand for transparency in the decision-making mechanisms of black-box algorithms has arisen. Alternatively, employing white-box algorithms can empower medical experts by allowing them to contribute their knowledge to the decision-making process and obtain a clear and transparent output. This approach offers an opportunity to personalize machine learning models through an agile process. A novel white-box machine learning algorithm known as Data canyons was employed as a transparent and robust foundation for the proposed solution. By providing medical experts with a web framework where their expertise is transferred to a machine learning model and enabling the utilization of this process in an agile manner, a symbiotic relationship is fostered between the domains of medical expertise and machine learning. The flexibility to manipulate the output machine learning model and visually validate it, even without expertise in machine learning, establishes a crucial link between these two expert domains.
Ključne besede:XAI, explainable artificial intelligence, data canyons, machine learning, transparency, agile development, white-box model
Status publikacije:Objavljeno
Verzija publikacije:Objavljena publikacija
Poslano v recenzijo:03.07.2023
Datum sprejetja članka:17.07.2023
Datum objave:19.07.2023
Založnik:MDPI
Leto izida:2023
Št. strani:Str. 1-12
Številčenje:Letn. 13, št. 14, št. članka 8329
PID:20.500.12556/DKUM-87387 Novo okno
UDK:004.5
COBISS.SI-ID:160320003 Novo okno
DOI:10.3390/app13148329 Novo okno
ISSN pri članku:2076-3417
Datum objave v DKUM:14.03.2024
Število ogledov:299
Število prenosov:34
Metapodatki:XML DC-XML DC-RDF
Področja:Ostalo
:
ŽLAHTIČ, Bojan, ZAVRŠNIK, Jernej, BLAŽUN VOŠNER, Helena, KOKOL, Peter, ŠURAN, David in ZAVRŠNIK, Tadej, 2023, Agile Machine Learning Model Development Using Data Canyons in Medicine : A Step towards Explainable Artificial Intelligence and Flexible Expert-Based Model Improvement. Applied sciences [na spletu]. 2023. Vol. 13, no. 14,  članka 8329, p. 1–12. [Dostopano 9 april 2025]. DOI 10.3390/app13148329. Pridobljeno s: https://dk.um.si/IzpisGradiva.php?lang=slv&id=87387
Kopiraj citat
  
Skupna ocena:
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
(0 glasov)
Vaša ocena:Ocenjevanje je dovoljeno samo prijavljenim uporabnikom.
Objavi na:Bookmark and Share


Postavite miškin kazalec na naslov za izpis povzetka. Klik na naslov izpiše podrobnosti ali sproži prenos.

Gradivo je del revije

Naslov:Applied sciences
Skrajšan naslov:Appl. sci.
Založnik:MDPI
ISSN:2076-3417
COBISS.SI-ID:522979353 Novo okno

Licence

Licenca:CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
Začetek licenciranja:19.07.2023

Sekundarni jezik

Jezik:Slovenski jezik
Ključne besede:umetna inteligenca, podatkovni kanjoni, strojno učenje, preglednost, agilni razvoj, model bele škatle


Komentarji

Dodaj komentar

Za komentiranje se morate prijaviti.

Komentarji (0)
0 - 0 / 0
 
Ni komentarjev!

Nazaj
Logotipi partnerjev Univerza v Mariboru Univerza v Ljubljani Univerza na Primorskem Univerza v Novi Gorici