Data harmonization and federated learning for multi-cohort dementia research using the OMOP common data model: : A Netherlands consortium of dementia cohorts case study
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Pedro Mateus, Justine Moonen, Magdalena Beran, Eva Jaarsma, Sophie M. van der Landen, Joost Heuvelink, Mahlet Birhanu, Alexander G.J. Harms, Esther Bron, Frank J. Wolters, Davy Cats, Hailiang Mei, Julie Oomens, Willemijn Jansen, Miranda T. Schram, Andre Dekker, Inigo BermejoAuthors Info & Claims
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References
[1]
V. Ehrenstein, H. Nielsen, A.B. Pedersen, S.P. Johnsen, L. Pedersen, Clinical epidemiology in the era of big data: new opportunities, familiar challenges, CLEP 9 (2017) 245–250.
[2]
T. Hulsen, et al., From big data to precision medicine, Front. Med. 6 (2019) 34.
[3]
J. Xu, et al., Federated learning for healthcare informatics, J. Healthc. Inform. Res. 5 (2021) 1–19.
[4]
S. Banabilah, M. Aloqaily, E. Alsayed, N. Malik, Y. Jararweh, Federated learning review: fundamentals, enabling technologies, and future applications, Inf. Process. Manag. 59 (2022).
[5]
I. Kholod, et al., Open-source federated learning frameworks for IoT: a comparative review and analysis, Sensors 21 (2020) 167.
[6]
E.A. Voss, et al., Feasibility and utility of applications of the common data model to multiple, disparate observational health databases, J. Am. Med. Inform. Assoc. 22 (2015) 553–564.
[7]
G. Hripcsak, et al., Observational Health Data Sciences and Informatics (OHDSI): opportunities for Observational Researchers, Stud. Health Technol. Inform. 216 (2015) 574–578.
[8]
J.C. Quiroz, et al., Extract, transform, load framework for the conversion of health databases to OMOP, PLoS One 17 (2022) e0266911.
[9]
WhiteRabbit and Rabbit-In-A-Hat (Version 0.10.8). OHDSI. https://github.com/OHDSI/WhiteRabbit/.
[10]
X. Zhou, et al., An evaluation of the THIN database in the OMOP common data model for active drug safety surveillance, Drug Saf. 36 (2013) 119–134.
[11]
S.T. Rosenbloom, R.J. Carroll, J.L. Warner, M.E. Matheny, J.C. Denny, Representing knowledge consistently across health systems, Yearb. Med. Inform. 26 (2017) 139–147.
[12]
B. Li, R. Tsui, How to improve the reuse of clinical data– openEHR and OMOP CDM, J. Phys. Conf. Ser. 1624 (2020).
[13]
V. Papez, et al., Transforming and evaluating the UK Biobank to the OMOP Common Data Model for COVID-19 research and beyond, J. Am. Med. Inform. Assoc. 30 (2022) 103–111.
[14]
Y. Yu, et al., Developing an ETL tool for converting the PCORnet CDM into the OMOP CDM to facilitate the COVID-19 data integration, J. Biomed. Inform. 127 (2022).
[15]
S.M.K. Sathappan, et al., Transformation of electronic health records and questionnaire data to OMOP CDM: a feasibility study using SG_T2DM dataset, Appl. Clin. Inform. 12 (2021) 757–767.
[16]
N. Paris, A. Lamer, A. Parrot, Transformation and evaluation of the MIMIC database in the OMOP common data model: development and usability study, JMIR Med. Inform. 9 (2021) e30970.
[17]
J.G. Klann, M.A.H. Joss, K. Embree, S.N. Murphy, Data model harmonization for the All Of Us Research Program: Transforming i2b2 data into the OMOP common data model, PLoS One 14 (2019).
[18]
J.R. Almeida, L.B. Silva, I. Bos, P.J. Visser, J.L. Oliveira, A methodology for cohort harmonisation in multicentre clinical research, Inf. Med. Unlocked 27 (2021).
[19]
A. Matcho, P. Ryan, D. Fife, C. Reich, Fidelity assessment of a clinical practice research datalink conversion to the OMOP common data model, Drug Saf. 37 (2014) 945–959.
[20]
M. Oja, et al., Transforming Estonian health data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model: lessons learned, JAMIA Open 6 (2023).
[21]
P. Biedermann, et al., Standardizing registry data to the OMOP Common Data Model: experience from three pulmonary hypertension databases, BMC Med. Res. Method. 21 (2021) 238.
[22]
D. Puttmann, N. De Keizer, R. Cornet, E. Van Der Zwan, F. Bakhshi-Raiez, FAIRifying a Quality Registry Using OMOP CDM: Challenges and Solutions, in: B. Séroussi, et al. (Eds.) Studies in Health Technology and Informatics, IOS Press, 2022. https://doi.org/10.3233/SHTI220476.
[23]
F. Cremonesi, et al., The need for multimodal health data modeling: a practical approach for a federated-learning healthcare platform, J. Biomed. Inform. 141 (2023).
[24]
G.H. Lee, et al., Feasibility study of federated learning on the distributed research network of OMOP common data model, Healthc. Inform. Res. 29 (2023) 168–173.
[25]
M.T. Schram, et al., The Maastricht Study: an extensive phenotyping study on determinants of type 2 diabetes, its complications and its comorbidities, Eur. J. Epidemiol. 29 (2014) 439–451.
[26]
C. Sun, J. Van Soest, M. Dumontier, Generating synthetic personal health data using conditional generative adversarial networks combining with differential privacy, J. Biomed. Inform. 143 (2023).
[27]
W.M. Van Der Flier, et al., Optimizing patient care and research: the Amsterdam dementia cohort, JAD 41 (2014) 313–327.
[28]
W. Verschuren, A. Blokstra, H. Picavet, H. Smit, Cohort profile: the doetinchem cohort study, Int. J. Epidemiol. 37 (2008) 1236–1241.
[29]
N. Legdeur, et al., Resilience to cognitive impairment in the oldest-old: design of the EMIF-AD 90+ study, BMC Geriatr. 18 (2018) 289.
[30]
E. Konijnenberg, et al., The EMIF-AD PreclinAD study: study design and baseline cohort overview, Alz Res Therapy 10 (2018) 75.
[31]
M. Schoenmaker, et al., Evidence of genetic enrichment for exceptional survival using a family approach: the Leiden Longevity Study, Eur. J. Hum. Genet. 14 (2006) 79–84.
[32]
M. Huisman, et al., Cohort profile: the longitudinal aging study Amsterdam, Int. J. Epidemiol. 40 (2011) 868–876.
[33]
M.M.B. Breteler, J.J. Claus, D.E. Grobbee, A. Hofman, Cardiovascular disease and distribution of cognitive function in elderly people: the Rotterdam study, BMJ 308 (1994) 1604–1608.
[34]
A.P. Appelman, et al., Total cerebral blood flow, white matter lesions and brain atrophy: the SMART-MR study, J. Cereb. Blood Flow Metab. 28 (2008) 633–639.
[35]
S.J. Reisinger, et al., Development and evaluation of a common data model enabling active drug safety surveillance using disparate healthcare databases, J. Am. Med. Inform. Assoc. 17 (2010) 652–662.
[36]
K.A. Spackman, K.E. Campbell, R.A. Côté, SNOMED RT: a reference terminology for health care, Proc AMIA Annu Fall Symp 640–644 (1997).
[37]
G. Shadow, C.J. McDonald, The Unified Code for Units of Measure, 2009. https://link.springer.com/chapter/10.1007/978-3-319-98192-5_37.
[38]
I. Bermejo, S. Vos European Medical Information Framework’s (EMIF) Alzheimer’s disease (AD) ontology, 2021.
[39]
V. Papez, et al., Transforming and evaluating electronic health record disease phenotyping algorithms using the OMOP common data model: a case study in heart failure, JAMIA Open 4 (2021) ooab001.
[40]
Athena (Version 1.11.0). OHDSI. https://github.com/OHDSI/Athena/.
[41]
Schuemie, M. Usagi (Version 1.3.0). OHDSI. https://github.com/OHDSI/Usagi/.
[42]
O. Beyan, et al., Distributed analytics on sensitive medical data: the personal health train, Data Intellegence 2 (2020) 96–107.
[43]
A. Moncada-Torres, F. Martin, M. Sieswerda, J. Van Soest, G. Geleijnse, VANTAGE6: an open source priVAcy preserviNg federaTed leArninG infrastructurE for Secure Insight eXchange, AMIA Annu. Symp. Proc. 2020 (2020) 870–877.
[44]
M.D. Wilkinson, et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci. Data 3 (2016).
[45]
M. Garza, G. Del Fiol, J. Tenenbaum, A. Walden, M.N. Zozus, Evaluating common data models for use with a longitudinal community registry, J. Biomed. Inform. 64 (2016) 333–341.
[46]
P. Mateus, et al., Federated BrainAge estimation from MRI: a proof of concept, Alzheimer’s & Dementia 19 (2023).
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Published: 24 July 2024
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