Current status of use of big data and artificial intelligence in RMDs: a systematic literature review informing EULAR recommendations.
Advisory Committees
/ organization & administration
Artificial Intelligence
/ trends
Big Data
Europe
/ epidemiology
Humans
Information Storage and Retrieval
/ trends
Machine Learning
/ statistics & numerical data
Musculoskeletal Diseases
/ epidemiology
Neural Networks, Computer
Publications
/ trends
Radiology
/ trends
Rheumatic Diseases
/ epidemiology
Sensitivity and Specificity
artificial intelligence
big data
biostatistics
machine learning
rheumatology
Journal
RMD open
ISSN: 2056-5933
Titre abrégé: RMD Open
Pays: England
ID NLM: 101662038
Informations de publication
Date de publication:
2019
2019
Historique:
received:
09
05
2019
revised:
26
06
2019
accepted:
29
06
2019
entrez:
16
8
2019
pubmed:
16
8
2019
medline:
16
8
2019
Statut:
epublish
Résumé
To assess the current use of big data and artificial intelligence (AI) in the field of rheumatic and musculoskeletal diseases (RMDs). A systematic literature review was performed in PubMed MEDLINE in November 2018, with key words referring to big data, AI and RMDs. All original reports published in English were analysed. A mirror literature review was also performed outside of RMDs on the same number of articles. The number of data analysed, data sources and statistical methods used (traditional statistics, AI or both) were collected. The analysis compared findings within and beyond the field of RMDs. Of 567 articles relating to RMDs, 55 met the inclusion criteria and were analysed, as well as 55 articles in other medical fields. The mean number of data points was 746 million (range 2000-5 billion) in RMDs, and 9.1 billion (range 100 000-200 billion) outside of RMDs. Data sources were varied: in RMDs, 26 (47%) were clinical, 8 (15%) biological and 16 (29%) radiological. Both traditional and AI methods were used to analyse big data (respectively, 10 (18%) and 45 (82%) in RMDs and 8 (15%) and 47 (85%) out of RMDs). Machine learning represented 97% of AI methods in RMDs and among these methods, the most represented was artificial neural network (20/44 articles in RMDs). Big data sources and types are varied within the field of RMDs, and methods used to analyse big data were heterogeneous. These findings will inform a European League Against Rheumatism taskforce on big data in RMDs.
Identifiants
pubmed: 31413871
doi: 10.1136/rmdopen-2019-001004
pii: rmdopen-2019-001004
pmc: PMC6668041
doi:
Types de publication
Comparative Study
Journal Article
Research Support, Non-U.S. Gov't
Systematic Review
Langues
eng
Pagination
e001004Déclaration de conflit d'intérêts
Competing interests: RC is an employee of Orange Healthcare, and HS is an employee of Sanoïa, a Digital CRO providing clinical research services including data science. There are no competing interests for the other authors.
Références
J Exp Clin Cancer Res. 2018 Dec 29;37(1):327
pubmed: 30594216
Rheumatol Int. 2019 Mar;39(3):403-416
pubmed: 30725156
N Engl J Med. 2016 Sep 29;375(13):1216-9
pubmed: 27682033
Front Genet. 2019 Feb 12;10:49
pubmed: 30809243
Ethn Dis. 2017 Apr 20;27(2):95-106
pubmed: 28439179
Ann Rheum Dis. 2020 Jan;79(1):69-76
pubmed: 31229952
PLoS Med. 2018 Dec 31;15(12):e1002721
pubmed: 30596635
Rheumatology (Oxford). 2018 Oct 1;57(57 Suppl 7):vii54-vii58
pubmed: 30289534
Rheum Dis Clin North Am. 2018 May;44(2):307-315
pubmed: 29622297
Biomed Res Int. 2015;2015:639021
pubmed: 26137488
Int J Obes (Lond). 2019 Dec;43(12):2573-2586
pubmed: 30655580
BJPsych Bull. 2017 Jun;41(3):129-132
pubmed: 28584647
BMC Ophthalmol. 2018 Nov 6;18(1):288
pubmed: 30400869
J Diabetes Sci Technol. 2019 Jan;13(1):123-127
pubmed: 30182736
Front Oncol. 2017 Aug 31;7:187
pubmed: 28913177
Nature. 2015 Nov 5;527(7576):S2-4
pubmed: 26536222
Sci Transl Med. 2018 Dec 12;10(471):
pubmed: 30541791
Methods Inf Med. 2015;54(6):546-7
pubmed: 26577624
Ann Card Anaesth. 2015 Jan-Mar;18(1):74-82
pubmed: 25566715
BMC Bioinformatics. 2018 Oct 15;19(Suppl 10):351
pubmed: 30367571
J Thorac Imaging. 2018 Jan;33(1):4-16
pubmed: 29252898
Kidney Res Clin Pract. 2017 Mar;36(1):3-11
pubmed: 28392994
Clin Rheumatol. 2017 Aug;36(8):1911-1917
pubmed: 28000011
Comput Biol Med. 2018 Apr 1;95:24-33
pubmed: 29433038
Ochsner J. 2007 Spring;7(1):3-7
pubmed: 21603472
Arthritis Care Res (Hoboken). 2019 Oct;71(10):1336-1343
pubmed: 30242992
Health Aff (Millwood). 2014 Jul;33(7):1212-9
pubmed: 25006148
J Med Syst. 2017 Oct 14;41(11):183
pubmed: 29032458
JMIR Public Health Surveill. 2016 Oct 11;2(2):e157
pubmed: 27729304
Public Health. 2018 Dec;165:9-15
pubmed: 30342281
Curr Opin Rheumatol. 2018 May;30(3):276-281
pubmed: 29369089
Medicine (Baltimore). 2019 Jan;98(3):e14146
pubmed: 30653149
Environ Int. 2018 Aug;117:284-291
pubmed: 29778013
IEEE Trans Biomed Eng. 2010 May;57(5):1143-51
pubmed: 20142161
Nat Rev Cardiol. 2016 Jun;13(6):350-9
pubmed: 27009423
Clin Ther. 2016 Apr;38(4):688-701
pubmed: 27130797
Nat Rev Genet. 2015 May;16(5):253-4
pubmed: 26065035
IEEE Trans Biomed Eng. 2017 Feb;64(2):263-273
pubmed: 27740470
Proc Natl Acad Sci U S A. 2018 Oct 30;115(44):11203-11208
pubmed: 30322910
World J Surg Oncol. 2019 Jan 8;17(1):12
pubmed: 30621704