Data Resource Profile: Nationwide registry data for high-throughput epidemiology and machine learning (FinRegistry)

Finn Gen; Essi Viippola; Sara Kuitunen; Rodosthenis S. Rodosthenous; Andrius Vabalas; Tuomo Hartonen; Pekka Vartiainen; Joanne Demmler; Anna-Leena Vuorinen; Aoxing Liu; Aki S. Havulinna; Vincent Llorens; Kira E. Detrois; Feiyi Wang; Matteo Ferro; Antti Karvanen; Jakob German; Sakari Jukarainen; Javier Gracia-Tabuenca; Tero Hiekkalinna; Sami Koskelainen; Tuomo Kiiskinen; Elisa Lahtela; Susanna Lemmelä; Teemu Paajanen; Harri Siirtola; Mary Pat Reeve; Kati Kristiansson; Minna Brunfeldt; Mervi Aavikko; Markus Perola; Andrea Ganna; Jaakko Kaprio

doi:10.1093/ije/dyad091

Data Resource Profile: Nationwide registry data for high-throughput epidemiology and machine learning (FinRegistry)

Finn Gen, Essi Viippola, Sara Kuitunen, Rodosthenis S. Rodosthenous, Andrius Vabalas, Tuomo Hartonen, Pekka Vartiainen, Joanne Demmler, Anna-Leena Vuorinen, Aoxing Liu, Aki S. Havulinna, Vincent Llorens, Kira E. Detrois, Feiyi Wang, Matteo Ferro, Antti Karvanen, Jakob German, Sakari Jukarainen, Javier Gracia-Tabuenca, Tero HiekkalinnaSami Koskelainen, Tuomo Kiiskinen, Elisa Lahtela, Susanna Lemmelä, Teemu Paajanen, Harri Siirtola, Mary Pat Reeve, Kati Kristiansson, Minna Brunfeldt, Mervi Aavikko, Markus Perola, Andrea Ganna, Jaakko Kaprio

Forskningsoutput: Tidskriftsbidrag › Artikel › Vetenskaplig › Peer review

Originalspråk	engelska
Tidskrift	International Journal of Epidemiology
Volym	52
Nummer	4
Sidor (från-till)	e195–e200
Antal sidor	6
ISSN	0300-5771
DOI	https://doi.org/10.1093/ije/dyad091
Status	Publicerad - 2 aug. 2023
MoE-publikationstyp	A1 Tidskriftsartikel-refererad

Vetenskapsgrenar

3142 Folkhälsovetenskap, miljö och arbetshälsa

Åtkomst av dokument

10.1093/ije/dyad091

dyad091Slutlig publicerad version, 733 KBLicens: CC BY
201153

Citera det här

Finn Gen, Viippola, E., Kuitunen, S., Rodosthenous, R. S., Vabalas, A., Hartonen, T., Vartiainen, P., Demmler, J., Vuorinen, A-L., Liu, A., Havulinna, A. S., Llorens, V., Detrois, K. E., Wang, F., Ferro, M., Karvanen, A., German, J., Jukarainen, S., Gracia-Tabuenca, J., ... Kaprio, J. (2023). Data Resource Profile: Nationwide registry data for high-throughput epidemiology and machine learning (FinRegistry). International Journal of Epidemiology, 52(4), e195–e200. https://doi.org/10.1093/ije/dyad091

@article{2f137f2df59a48979e8cefd19c6c8dfb,

title = "Data Resource Profile: Nationwide registry data for high-throughput epidemiology and machine learning (FinRegistry)",

keywords = "Models, 3142 Public health care science, environmental and occupational health",

author = "{Finn Gen} and Essi Viippola and Sara Kuitunen and Rodosthenous, {Rodosthenis S.} and Andrius Vabalas and Tuomo Hartonen and Pekka Vartiainen and Joanne Demmler and Anna-Leena Vuorinen and Aoxing Liu and Havulinna, {Aki S.} and Vincent Llorens and Detrois, {Kira E.} and Feiyi Wang and Matteo Ferro and Antti Karvanen and Jakob German and Sakari Jukarainen and Javier Gracia-Tabuenca and Tero Hiekkalinna and Sami Koskelainen and Tuomo Kiiskinen and Elisa Lahtela and Susanna Lemmel{\"a} and Teemu Paajanen and Harri Siirtola and Reeve, {Mary Pat} and Kati Kristiansson and Minna Brunfeldt and Mervi Aavikko and Markus Perola and Andrea Ganna and Jaakko Kaprio",

year = "2023",

month = aug,

day = "2",

doi = "10.1093/ije/dyad091",

language = "English",

volume = "52",

pages = "e195–e200",

journal = "International Journal of Epidemiology",

issn = "0300-5771",

publisher = "Oxford University Press",

number = "4",

}

Finn Gen, Viippola, E, Kuitunen, S, Rodosthenous, RS , Vabalas, A , Hartonen, T, Vartiainen, P, Demmler, J , Vuorinen, A-L , Liu, A , Havulinna, AS , Llorens, V, Detrois, KE, Wang, F, Ferro, M, Karvanen, A, German, J, Jukarainen, S, Gracia-Tabuenca, J, Hiekkalinna, T, Koskelainen, S, Kiiskinen, T , Lahtela, E , Lemmelä, S, Paajanen, T, Siirtola, H, Reeve, MP, Kristiansson, K, Brunfeldt, M, Aavikko, M , Perola, M , Ganna, A & Kaprio, J 2023, 'Data Resource Profile: Nationwide registry data for high-throughput epidemiology and machine learning (FinRegistry)', International Journal of Epidemiology, vol. 52, nr. 4, s. e195–e200. https://doi.org/10.1093/ije/dyad091

TY - JOUR

T1 - Data Resource Profile

T2 - Nationwide registry data for high-throughput epidemiology and machine learning (FinRegistry)

AU - Finn Gen

AU - Viippola, Essi

AU - Kuitunen, Sara

AU - Rodosthenous, Rodosthenis S.

AU - Vabalas, Andrius

AU - Hartonen, Tuomo

AU - Vartiainen, Pekka

AU - Demmler, Joanne

AU - Vuorinen, Anna-Leena

AU - Liu, Aoxing

AU - Havulinna, Aki S.

AU - Llorens, Vincent

AU - Detrois, Kira E.

AU - Wang, Feiyi

AU - Ferro, Matteo

AU - Karvanen, Antti

AU - German, Jakob

AU - Jukarainen, Sakari

AU - Gracia-Tabuenca, Javier

AU - Hiekkalinna, Tero

AU - Koskelainen, Sami

AU - Kiiskinen, Tuomo

AU - Lahtela, Elisa

AU - Lemmelä, Susanna

AU - Paajanen, Teemu

AU - Siirtola, Harri

AU - Reeve, Mary Pat

AU - Kristiansson, Kati

AU - Brunfeldt, Minna

AU - Aavikko, Mervi

AU - Perola, Markus

AU - Ganna, Andrea

AU - Kaprio, Jaakko

PY - 2023/8/2

Y1 - 2023/8/2

KW - Models

KW - 3142 Public health care science, environmental and occupational health

U2 - 10.1093/ije/dyad091

DO - 10.1093/ije/dyad091

M3 - Article

C2 - 37365732

VL - 52

SP - e195–e200

JO - International Journal of Epidemiology

JF - International Journal of Epidemiology

SN - 0300-5771

IS - 4

ER -