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Naslov:FooDis : a food-disease relation mining pipeline
Avtorji:ID Cenikj, Gjorgjina, Institut Jožef Stefan (Avtor)
ID Eftimov, Tome, Institut Jožef Stefan (Avtor)
ID Koroušić-Seljak, Barbara, Institut Jožef Stefan (Avtor)
Datoteke:URL URL - Izvorni URL, za dostop obiščite https://www.sciencedirect.com/science/article/pii/S0933365723001008
 
.pdf PDF - Predstavitvena datoteka, prenos (1,11 MB)
MD5: 2502DA6E1E37E77244654DB1A30DCEBB
 
Jezik:Angleški jezik
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:Logo IJS - Institut Jožef Stefan
Povzetek:Nowadays, it is really important and crucial to follow the new biomedical knowledge that is presented in scientific literature. To this end, Information Extraction pipelines can help to automatically extract meaningful relations from textual data that further require additional checks by domain experts. In the last two decades, a lot of work has been performed for extracting relations between phenotype and health concepts, however, the relations with food entities which are one of the most important environmental concepts have never been explored. In this study, we propose FooDis, a novel Information Extraction pipeline that employs state-of-the-art approaches in Natural Language Processing to mine abstracts of biomedical scientific papers and automatically suggests potential cause or treat relations between food and disease entities in different existing semantic resources. A comparison with already known relations indicates that the relations predicted by our pipeline match for 90% of the food-disease pairs that are common in our results and the NutriChem database, and 93% of the common pairs in the DietRx platform. The comparison also shows that the FooDis pipeline can suggest relations with high precision. The FooDis pipeline can be further used to dynamically discover new relations between food and diseases that should be checked by domain experts and further used to populate some of the existing resources used by NutriChem and DietRx.
Ključne besede:text mining, relation extraction, named entity recognition, named entity linking, food-disease relations
Status publikacije:V tisku
Verzija publikacije:Recenzirani rokopis
Poslano v recenzijo:11.04.2023
Datum sprejetja članka:16.05.2023
Datum objave:20.05.2023
Založnik:Elsevier
Leto izida:2023
Št. strani:str. 1-48
Številčenje:Vol. , [article no.] 102586
Izvor:Nizozemska
PID:20.500.12556/DiRROS-16585 Novo okno
UDK:004.8
ISSN pri članku:1873-2860
DOI:10.1016/j.artmed.2023.102586 Novo okno
COBISS.SI-ID:153384451 Novo okno
Avtorske pravice:© 2023 The Author(s).
Opomba:Nasl. z nasl. zaslona; Opis vira z dne 25. 5. 2023;
Datum objave v DiRROS:25.05.2023
Število ogledov:846
Število prenosov:506
Metapodatki:XML DC-XML DC-RDF
:
CENIKJ, Gjorgjina, EFTIMOV, Tome in KOROUŠIĆ-SELJAK, Barbara, 2023, FooDis : a food-disease relation mining pipeline. Artificial intelligence in medicine [na spletu]. 2023. Vol. 102586, p. 1–48. [Dostopano 6 april 2025]. DOI 10.1016/j.artmed.2023.102586. Pridobljeno s: https://dirros.openscience.si/IzpisGradiva.php?lang=slv&id=16585
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Gradivo je del revije

Naslov:Artificial intelligence in medicine
Založnik:Elsevier
ISSN:1873-2860
COBISS.SI-ID:23192325 Novo okno

Gradivo je financirano iz projekta

Financer:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Številka projekta:P2-0098
Naslov:Računalniške strukture in sistemi
Financer:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Program financ.:Shema mladega raziskovalca
Številka projekta:PR-12393
Financer:EC - European Commission
Program financ.:H2020
Številka projekta:101005259
Naslov:Communities on Food Consumer Science
Akronim:COMFOCUS
Financer:EC - European Commission
Program financ.:H2020
Številka projekta:863059
Naslov:Food Nutrition Security Cloud
Akronim:FNS-Cloud
Financer:Drugi - Drug financer ali več financerjev
Program financ.:Support for Automating some specific steps of Systematic Review process using Artificial Intelligence
Številka projekta:GP/EFSA/AMU/2020/03/LOT2
Akronim:CAFETERIA

Licence

Licenca:CC BY-NC-ND 4.0, Creative Commons Priznanje avtorstva-Nekomercialno-Brez predelav 4.0 Mednarodna
Povezava:http://creativecommons.org/licenses/by-nc-nd/4.0/deed.sl
Opis:Najbolj omejujoča licenca Creative Commons. Uporabniki lahko prenesejo in delijo delo v nekomercialne namene in ga ne smejo uporabiti za nobene druge namene.
Začetek licenciranja:20.05.2023

Sekundarni jezik

Jezik:Slovenski jezik
Naslov:FooDis: a food-disease relation mining pipeline
Ključne besede:besedilno rudarjenje, luščenje povezav, prepoznavanje poimenovanih enitet, povezovanje poimenovanih entitet, prehrana, bolezni


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