Treating medical data as a durable asset.
Journal
Nature genetics
ISSN: 1546-1718
Titre abrégé: Nat Genet
Pays: United States
ID NLM: 9216904
Informations de publication
Date de publication:
10 2020
10 2020
Historique:
received:
30
03
2020
accepted:
21
08
2020
pubmed:
16
9
2020
medline:
25
11
2020
entrez:
15
9
2020
Statut:
ppublish
Résumé
Access to medical data is central for conducting research on genomics. However, to tap these metadata (observable traits and phenotypes, diagnoses and medication, and labels), researchers must grapple with the complex and sensitive nature of the information. In this Perspective, we argue that, at this exciting time for genomics and artificial intelligence, several critical aspects of data generation, infrastructure and management are pillars of a modern data ecosystem. Many risks to privacy and many obstacles to medical research can be eliminated or mitigated by new secure data analytics. Finally, we discuss the potential consequences of medical data exiting the institutions and being managed by individuals. These shifts in data ownership have the potential for profound disruption and opportunity across many fields.
Identifiants
pubmed: 32929286
doi: 10.1038/s41588-020-0698-y
pii: 10.1038/s41588-020-0698-y
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
1005-1010Commentaires et corrections
Type : ErratumIn
Références
Telenti, A. Machine learning to decode genomics. Clin. Chem. 66, 45–47 (2020).
pubmed: 31601562
Zou, J. et al. A primer on deep learning in genomics. Nat. Genet. 51, 12–18 (2019).
pubmed: 30478442
Morgan, E. & Prowle, M. (eds.) Financial Management and Control in Higher Education (Taylor & Francis, 2004).
Shomorony, I. et al. An unsupervised learning approach to identify novel signatures of health and disease from multimodal data. Genome Med. 12, 7 (2020).
pubmed: 31924279
pmcid: 6953286
Krumm, N. & Hoffman, N. Practical estimation of cloud storage costs for clinical genomic data. Pr. Lab Med 21, e00168 (2020).
Banks, M. A. Sizing up big data. Nat. Med. 26, 5–6 (2020).
pubmed: 31932781
Telenti, A., Steinhubl, S. R. & Topol, E. J. Rethinking the medical record. Lancet 391, 1013 (2018).
pubmed: 29565009
Harerimana, G., Kim, J. W., Yoo, H. & Jang, B. Deep learning for electronic health records analytics. IEEE Access 7, 101245–101259 (2019).
Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. BERT: pre-training of deep bidirectional transformers for language understanding. in Proc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Association for Computational Linguistics, 2019).
Telenti, A. Council post: do we need more data or more science in data science? Forbes (20 February 2020).
Hert, P. D. et al. The right to data portability in the GDPR: towards user-centric interoperability of digital services. Comput. Law Secur. Rev. 34, 193–203 (2018).
Forcier, M. B., Gallois, H., Mullan, S. & Joly, Y. Integrating artificial intelligence into health care through data access: can the GDPR act as a beacon for policymakers? J. Law Biosci. 6, 317–335 (2019).
pubmed: 31666972
pmcid: 6813940
Institutions that support health records on iPhone and iPod touch. Apple.com https://support.apple.com/en-us/HT208647 (2020).
Marchant, G., Barnes, M., Evans, J. P., LeRoy, B. & Wolf, S. M. From genetics to genomics: facing the liability implications in clinical care. J. Law Med. Ethics 48, 11–43 (2020).
pubmed: 32342786
pmcid: 7433684
Ienca, M. et al. Considerations for ethics review of big data health research: a scoping review. PLoS ONE 13, e0204937 (2018).
pubmed: 30308031
pmcid: 6181558
Goodman, K., Zandi, D., Reis, A. & Vayena, E. Balancing risks and benefits of artificial intelligence in the health sector. Bull. World Health Organ. 98, 230–230A (2020).
pubmed: 32284640
pmcid: 7133475
Pan, X., Zhang, M., Ji, S. & Yang, M. Privacy risks of general-purpose language models. in 2020 IEEE Symposium on Security and Privacy 1314–1331 (IEEE, 2020).
Number of mHealth apps available in the Apple App Store from 1st quarter 2015 to 1st quarter 2020. Statista.com https://www.statista.com/statistics/779910/health-apps-available-ios-worldwide (2020).
Hathaway, L. National Policy on the Use of the Advanced Encryption Standard (AES) to Protect National Security Systems and National Security Information. CNSS Policy 15, Fact Sheet 1 (National Security Agency, 2003); https://csrc.nist.gov/csrc/media/projects/cryptographic-module-validation-program/documents/cnss15fs.pdf
Jiang, X., Kim, M., Lauter, K. & Song, Y. Secure outsourced matrix computation and application to neural networks. Conf. Comput Commun. Secur 2018, 1209–1222 (2018).
pubmed: 31404438
pmcid: 6689419
Kim, M. & Lauter, K. Private genome analysis through homomorphic encryption. BMC Med. Inform. Decis. Mak. 15, S3 (2015). (Suppl. 5).
pubmed: 26733152
pmcid: 4699052
Hie, B., Cho, H. & Berger, B. Realizing private and practical pharmacological collaboration. Science 362, 347–350 (2018).
pubmed: 30337410
pmcid: 6519716
Cho, H., Wu, D. J. & Berger, B. Secure genome-wide association analysis using multiparty computation. Nat. Biotechnol. 36, 547–551 (2018).
pubmed: 29734293
pmcid: 5990440
McLaren, P. J. et al. Privacy-preserving genomic testing in the clinic: a model using HIV treatment. Genet. Med. 18, 814–822 (2016).
pubmed: 26765343
pmcid: 4985613
Chen, F. et al. PRINCESS: privacy-protecting rare disease international network collaboration via encryption through software guard extensions. Bioinformatics 33, 871–878 (2017).
pubmed: 28065902
Kockan, C. et al. Sketching algorithms for genomic data analysis and querying in a secure enclave. Nat. Meth. 17, 295–301 (2020).
Raisaro, J. L. et al. MedCo: enabling secure and privacy-preserving exploration of distributed clinical and genomic data. IEEE/ACM Trans. Comput. Biol. Bioinform. 16, 1328–1341 (2019).
pubmed: 30010584
Raisaro, J.L. et al. SCOR: a secure international informatics infrastructure to investigate COVID-19. JAMA https://doi.org/10.1093/jamia/ocaa172 (2020).
Bergeron, J. et al. Simulating patient matching to clinical trials using a property rights blockchain. Digit. Med. 6, 44–52 (2020).