Axes of a revolution: challenges and promises of big data in healthcare

Nature Medicine

Volumn 26, Issue 1, 2020, Pages 29-38

  Shilo, Smadar ^a,b   Rossman, Hagai ^a   Segal, Eran ^a  

^a WEIZMANN INSTITUTE OF SCIENCE   (Israel)

^b Rambam Health Care Campus   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

BIG DATA; COHORT ANALYSIS; DATA ANALYSIS; DEMOGRAPHY; DESCRIPTIVE RESEARCH; DIAGNOSIS; DISEASE EXACERBATION; DISEASE MODEL; ENVIRONMENTAL FACTOR; FOLLOW UP; GENETIC ASSOCIATION; HEALTH CARE; HUMAN; INFORMATION PROCESSING; LONGITUDINAL STUDY; MEDICINE; PERSONALIZED MEDICINE; PHENOTYPE; PREDICTION; PRIORITY JOURNAL; PROGNOSIS; PROPHYLAXIS; REVIEW; SAMPLE SIZE; SOCIAL INTERACTION; STANDARDIZATION; HEALTH CARE DELIVERY; STANDARD;

BIG DATA; COHORT STUDIES; DELIVERY OF HEALTH CARE; DISEASE PROGRESSION; HUMANS; MEDICINE; REFERENCE STANDARDS;

EID: 85077786018     PISSN: 10788956     EISSN: 1546170X     Source Type: Journal    
DOI: 10.1038/s41591-019-0727-5     Document Type: Review

References (128)

1. Grad, F. P. The Preamble of the Constitution of the World Health Organization. Bull. World Health Organ. 80, 981 (2002).
2. Burton-Jeangros, C., Cullati, S., Sacker, A. & Blane, D. A Life Course Perspective on Health Trajectories and Transitions Vol. 4 pp. 1–18 (Springer, 2015); https://link.springer.com/chapter/10.1007/978-3-319-20484-0_1
3. Obermeyer, Z. & Emanuel, E. J. Predicting the future—big data, machine learning, and clinical medicine. N. Engl. J. Med. 375, 1216–1219 (2016).
4. Benke, K. & Benke, G. Artificial intelligence and big data in public health. Int. J. Environ. Res. Public Health 15, E2796 (2018).
5. Baro, E., Degoul, S., Beuscart, R. & Chazard, E. Toward a literature-driven definition of big data in healthcare. BioMed. Res. Int. 2015, 639021 (2015).
6. Gligorijević, V., Malod-Dognin, N. & Pržulj, N. Integrative methods for analyzing big data in precision medicine. Proteomics 16, 741–758 (2016).
7. Cios, K. J. & Moore, G. W. Uniqueness of medical data mining. Artif. Intell. Med. 26, 1–24 (2002).
8. Rumsfeld, J. S., Joynt, K. E. & Maddox, T. M. Big data analytics to improve cardiovascular care: promise and challenges. Nat. Rev. Cardiol. 13, 350–359 (2016).
9. Koopmans, R. & Schaeffer, M. Relational diversity and neighbourhood cohesion. Unpacking variety, balance and in-group size. Soc. Sci. Res. 53, 162–176 (2015).
10. Gould, A. L. Planning and revising the sample size for a trial. Stat. Med. 14, 1039–1051 (1995).
11. Booker, C. L., Harding, S. & Benzeval, M. A systematic review of the effect of retention methods in population-based cohort studies. BMC Public Health 11, 249 (2011).
12. Mason, C. E., Porter, S. G. & Smith, T. M. Characterizing multi-omic data in systems biology. Adv. Exp. Med. Biol. 799, 15–38 (2014).
13. Cho, I. & Blaser, M. J. The human microbiome: at the interface of health and disease. Nat. Rev. Genet. 13, 260–270 (2012).
14. Palsson, B. & Zengler, K. The challenges of integrating multi-omic data sets. Nat. Chem. Biol. 6, 787–789 (2010).
15. Check Hayden, E. Is the $1,000 genome for real? Nature https://www.nature.com/news/is-the-1-000-genome-for-real-1.14530 (2014).
16. Kwon, E. J. & Kim, Y. J. What is fetal programming?: a lifetime health is under the control of in utero health. Obstet. Gynecol. Sci. 60, 506–519 (2017).
17. Barker, D. J. In utero programming of chronic disease. Clin. Sci. 95, 115–128 (1998).
18. Topol, E. J. Individualized medicine from prewomb to tomb. Cell 157, 241–253 (2014).
19. Qiu, X. et al. The born in guangzhou cohort study (BIGCS). Eur. J. Epidemiol. 32, 337–346 (2017).
20. Golding, J., Pembrey M., Jones, R. & ALSPAC Study Team. ALSPAC—The Avon Longitudinal Study of Parents and Children. Paediatr. Perinat. Epidemiol. 15, 74–87 (2001).
21. Howe, C. J., Cole, S. R., Lau, B., Napravnik, S. & Eron, J. J. Jr. Selection bias due to loss to follow up in cohort studies. Epidemiology 27, 91–97 (2016).
22. Swanson, J. M. The UK Biobank and selection bias. Lancet 380, 110 (2012).
23. Brieger, K. et al. Genes for Good: engaging the public in genetics research via social media. Am. J. Hum. Genet. 105, 65–77 (2019).
24. Kaprio, J. The Finnish Twin Cohort Study: an update. Twin Res. Hum. Genet. 16, 157–162 (2013).
25. Magnus, P. et al. Cohort profile update: the Norwegian mother and child cohort study (MoBa). Int. J. Epidemiol. 45, 382–388 (2016).
26. Beesley, L. J. et al. The emerging landscape of health research based on biobanks linked to electronic health records: existing resources, statistical challenges, and potential opportunities. Stat. Med. 10.1002/sim.8445 (2019).
27. Lau, B., Gange, S. J. & Moore, R. D. Interval and clinical cohort studies: epidemiological issues. AIDS Res. Hum. Retroviruses 23, 769–776 (2007).
28. Chen, M. S. Jr., Lara, P. N., Dang, J. H. T., Paterniti, D. A. & Kelly, K. Twenty years post-NIH Revitalization Act: enhancing minority participation in clinical trials (EMPaCT): laying the groundwork for improving minority clinical trial accrual: renewing the case for enhancing minority participation in cancer clinical trials. Cancer 120, 1091–1096 (2014).
29. Popejoy, A. B. & Fullerton, S. M. Genomics is failing on diversity. Nature 538, 161–164 (2016).
30. Mahmood, S. S., Levy, D., Vasan, R. S. & Wang, T. J. The Framingham Heart Study and the epidemiology of cardiovascular disease: a historical perspective. Lancet 383, 999–1008 (2014).
31. Colditz, G. A., Manson, J. E. & Hankinson, S. E. The Nurses’ Health Study: 20-year contribution to the understanding of health among women. J. Women’s Health 6, 49–62 (1997).
32. Liao, Y., McGee, D. L., Cooper, R. S. & Sutkowski, M. B. How generalizable are coronary risk prediction models? Comparison of Framingham and two national cohorts. Am. Heart J. 137, 837–845 (1999).
33. Denny, J. C. et al. The “All of Us” Research Program. N. Engl. J. Med. 381, 668–676 (2019).
34. Hripcsak, G. et al. Observational Health Data Sciences and Informatics (OHDSI): opportunities for observational researchers. Stud. Health Technol. Inform. 216, 574–578 (2015).
35. Rajkomar, A. et al. Scalable and accurate deep learning with electronic health records. npj Digit. Med. 1, 18 (2018).
36. Vashisht, R. et al. Association of hemoglobin a1c levels with use of sulfonylureas, dipeptidyl peptidase 4 inhibitors, and thiazolidinediones in patients with type 2 diabetes treated with metformin: analysis from the observational health data sciences and informatics initiative. JAMA Netw. Open 1, e181755 (2018).
37. Gebru, T. et al. Datasheets for datasets. arXiv 1803.09010 (2018).
38. Sudlow, C. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).
39. Wolford, B. N., Willer, C. J. & Surakka, I. Electronic health records: the next wave of complex disease genetics. Hum. Mol. Genet. 27, R14–R21 (2018).
40. Weber, G. M., Mandl, K. D. & Kohane, I. S. Finding the missing link for big biomedical data. J. Am. Med. Assoc. 311, 2479–2480 (2014).
41. Evans, R. S. Electronic health records: then, now, and in the future. Yearb. Med. Inform. 1, S48–S61 (2016).
42. Tiik, M. & Ross, P. Patient opportunities in the Estonian electronic health record system. Stud. Health Technol. Inform. 156, 171–177 (2010).
43. Montgomery, J. Data sharing and the idea of ownership. New Bioeth. 23, 81–86 (2017).
44. Rodwin, M. A. The case for public ownership of patient data. J. Am. Med. Assoc. 302, 86–88 (2009).
45. Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).
46. Hewitt, R. & Watson, P. Defining biobank. Biopreserv. Biobank. 11, 309–315 (2013).
47. Organization for Economic Cooperation and Development. Glossary of Statistical Terms: Biobank. in Creation and Governance of Human Genetic Research Databases (OECD). https://stats.oecd.org/glossary/detail.asp?ID=7220 (2006).
48. Kinkorová, J. Biobanks in the era of personalized medicine: objectives, challenges, and innovation: Overview. EPMA J. 7, 4 (2016).
49. Gaziano, J. M. et al. Million Veteran Program: A mega-biobank to study genetic influences on health and disease. J. Clin. Epidemiol. 70, 214–223 (2016).
50. Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).
51. Chen, Z. et al. China Kadoorie Biobank of 0.5 million people: survey methods, baseline characteristics and long-term follow-up. Int. J. Epidemiol. 40, 1652–1666 (2011).
52. Tapia-Conyer, R. et al. Cohort profile: the Mexico City Prospective Study. Int. J. Epidemiol. 35, 243–249 (2006).
53. Senn, S. Statistical pitfalls of personalized medicine. Nature 563, 619–621 (2018).
54. Garrett-Bakelman, F. E. et al. The NASA Twins Study: A multidimensional analysis of a year-long human spaceflight. Science 364, eaau8650 (2019).
55. Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 1079–1094 (2015).
56. Rothschild, D. et al. Environment dominates over host genetics in shaping human gut microbiota. Nature 555, 210–215 (2018).
57. Zeevi, D. et al. Structural variation in the gut microbiome associates with host health. Nature 568, 43–48 (2019).
58. Shah, T. et al. Population genomics of cardiometabolic traits: design of the University College London-London School of Hygiene and Tropical Medicine-Edinburgh-Bristol (UCLEB) Consortium. PLoS One 8, e71345 (2013).
59. Tigchelaar, E. F. et al. Cohort profile: LifeLines DEEP, a prospective, general population cohort study in the northern Netherlands: study design and baseline characteristics. BMJ Open 5, e006772 (2015).
60. Cohen, I.G. & Mello, M.M. Big data, big tech, and protecting patient privacy. J. Am. Med. Assoc. 322, 1141–1142 (2019).
61. Price, W. N. II & Cohen, I. G. Privacy in the age of medical big data. Nat. Med. 25, 37–43 (2019).
62. Tutton, R., Kaye, J. & Hoeyer, K. Governing UK Biobank: the importance of ensuring public trust. Trends Biotechnol. 22, 284–285 (2004).
63. Kaufman, D. J., Murphy-Bollinger, J., Scott, J. & Hudson, K. L. Public opinion about the importance of privacy in biobank research. Am. J. Hum. Genet. 85, 643–654 (2009).
64. Hernán, M. A., Hsu, J. & Healy, B. A second chance to get causal inference right: a classification of data science tasks. Chance 32, 42–49 (2019).
65. Shmueli, G. To Explain or to Predict? Stat. Sci. 25, 289–310 (2010).
66. Geserick, M. et al. Acceleration of BMI in early childhood and risk of sustained obesity. N. Engl. J. Med. 379, 1303–1312 (2018).
67. Obermeyer, Z., Samra, J. K. & Mullainathan, S. Individual differences in normal body temperature: longitudinal big data analysis of patient records. Br. Med. J. 359, j5468 (2017).
68. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
69. Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 84–90 (2017).
70. Goldberger, A. L. et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101, E215–E220 (2000).
71. Johnson, A. E. W. et al. MIMIC-III, a freely accessible critical care database. Sci. Data 3, 160035 (2016).
72. Wang, S. et al. MIMIC-Extract: a data extraction, preprocessing, and representation pipeline for MIMIC-III. arXiv 1907.08322 (2019).
73. Ching, T. et al. Opportunities and obstacles for deep learning in biology and medicine. J. R. Soc. Interface 15, 20170387 (2018).
74. Ghassemi, M., Naumann, T., Schulam, P., Beam, A. L. & Ranganath, R. Opportunities in machine learning for healthcare. arXiv 1806.00388 (2018).
75. Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44–56 (2019).
76. Rajkomar, A., Dean, J. & Kohane, I. Machine learning in medicine. N. Engl. J. Med. 380, 1347–1358 (2019).
77. Bengio, Y., Courville, A. & Vincent, P. Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1798–1828 (2013).
78. Weng, W.H. & Szolovits, P. Representation learning for electronic health records. arXiv 1909.09248 (2019).
79. Dickerman, B. A., García-Albéniz, X., Logan, R. W., Denaxas, S. & Hernán, M. A. Avoidable flaws in observational analyses: an application to statins and cancer. Nat. Med. 25, 1601–1606 (2019).
80. Agniel, D., Kohane, I. S. & Weber, G. M. Biases in electronic health record data due to processes within the healthcare system: retrospective observational study. Br. Med. J. 361, k1479 (2018).
81. Hernán, M. A. & Robins, J. M. Using big data to emulate a target trial when a randomized trial is not available. Am. J. Epidemiol. 183, 758–764 (2016).
82. Pearl, J. Causality (Cambridge University Press, 2009).
83. Johansson, F., Shalit, U. & Sontag, D. Learning representations for counterfactual inference. arXiv 1605.03661 (2016).
84. Dickerman, B. A., García-Albéniz, X., Logan, R. W., Denaxas, S. & Hernán, M. A. Avoidable flaws in observational analyses: an application to statins and cancer. Nat. Med. 25, 1601–1606 (2019).
85. Smith, G. D. & Ebrahim, S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int. J. Epidemiol. 32, 1–22 (2003).
86. Hu, P., Jiao, R., Jin, L. & Xiong, M. Application of causal inference to genomic analysis: advances in methodology. Front. Genet. 9, 238 (2018).
87. Yusuf, S. et al. Modifiable risk factors, cardiovascular disease, and mortality in 155 722 individuals from 21 high-income, middle-income, and low-income countries (PURE): a prospective cohort study. Lancet 10.1016/S0140-6736(19)32008-2 (2019).
88. Tomašev, N. et al. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature 572, 116–119 (2019).
89. Rivers, E. et al. Early goal-directed therapy in the treatment of severe sepsis and septic shock. N. Engl. J. Med. 345, 1368–1377 (2001).
90. Calvert, J. S. et al. A computational approach to early sepsis detection. Comput. Biol. Med. 74, 69–73 (2016).
91. Shimabukuro, D. W., Barton, C. W., Feldman, M. D., Mataraso, S. J. & Das, R. Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial. BMJ Open Respir. Res. 4, e000234 (2017).
92. Avati, A. et al. Improving palliative care with deep learning. BMC Med. Inform. Decis. Mak. 18, 122 (2018).
93. Lipton, Z. C. The mythos of model interpretability. Commun. ACM 61, 36–43 (2018).
94. Vogt, H., Green, S., Ekstrøm, C. T. & Brodersen, J. How precision medicine and screening with big data could increase overdiagnosis. Br. Med. J. 366, l5270 (2019).
95. Obermeyer, Z., Powers, B., Vogeli, C. & Mullainathan, S. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 447–453 (2019).
96. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care 36, S67–S74 (2013).
97. Udler, M. S. et al. Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: A soft clustering analysis. PLoS Med. 15, e1002654 (2018).
98. Young, A. I., Benonisdottir, S., Przeworski, M. & Kong, A. Deconstructing the sources of genotype-phenotype associations in humans. Science 365, 1396–1400 (2019).
99. Lakhani, C. M. et al. Repurposing large health insurance claims data to estimate genetic and environmental contributions in 560 phenotypes. Nat. Genet. 51, 327–334 (2019).
100. Kohane, I. S. Using electronic health records to drive discovery in disease genomics. Nat. Rev. Genet. 12, 417–428 (2011).
101. Phelan, M., Bhavsar, N. & Goldstein, B. A. Illustrating informed presence bias in electronic health records data: how patient interactions with a healthsystem can impact inference. eGEMs 5, 22 (2017).
102. Brodniewicz, T. & Grynkiewicz, G. Preclinical drug development. Acta Pol. Pharm. 67, 578–585 (2010).
103. Breyer, M. D. Improving productivity of modern-day drug discovery. Expert Opin. Drug Discov. 9, 115–118 (2014).
104. FitzGerald, G. et al. The future of humans as model organisms. Science 361, 552–553 (2018).
105. Matthews, H., Hanison, J. & Nirmalan, N. “Omics”-informed drug and biomarker discovery: opportunities, challenges and future perspectives. Proteomes 4, 28 (2016).
106. Reshef, D. N. et al. Detecting novel associations in large data sets. Science 334, 1518–1524 (2011).
107. Finan, C. et al. The druggable genome and support for target identification and validation in drug development. Sci. Transl. Med. 9, eaag1166 (2017).
108. Nelson, M. R. et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47, 856–860 (2015).
109. Pushpakom, S. et al. Drug repurposing: progress, challenges and recommendations. Nat. Rev. Drug Discov. 18, 41–58 (2019).
110. Paik, H. et al. Repurpose terbutaline sulfate for amyotrophic lateral sclerosis using electronic medical records. Sci. Rep. 5, 8580 (2015).
111. Dudley, J. T. et al. Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Sci. Transl. Med. 3, 96ra76 (2011).
112. Xu, S. et al. Prevalence and predictability of low-yield inpatient laboratory diagnostic tests. JAMA Netw. Open 2, e1910967 (2019).
113. Einav, L., Finkelstein, A., Mullainathan, S. & Obermeyer, Z. Predictive modeling of U.S. health care spending in late life. Science 360, 1462–1465 (2018).
114. Ahlqvist, E. et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 6, 361–369 (2018).
115. Thenganatt, M. A. & Jankovic, J. Parkinson disease subtypes. JAMA Neurol. 71, 499–504 (2014).
116. Lawton, M. et al. Developing and validating Parkinson’s disease subtypes and their motor and cognitive progression. J. Neurol. Neurosurg. Psychiatry 89, 1279–1287 (2018).
117. Berg, D. et al. Time to redefine PD? Introductory statement of the MDS Task Force on the definition of Parkinson’s disease. Mov. Disord. 29, 454–462 (2014).
118. Collins, F. S. & Varmus, H. A new initiative on precision medicine. N. Engl. J. Med. 372, 793–795 (2015).
119. Awadalla, P. et al. Cohort profile of the CARTaGENE study: Quebec’s population-based biobank for public health and personalized genomics. Int. J. Epidemiol. 42, 1285–1299 (2013).
120. Scholtens, S. et al. Cohort Profile: LifeLines, a three-generation cohort study and biobank. Int. J. Epidemiol. 44, 1172–1180 (2015).
121. Christensen, H., Nielsen, J. S., Sørensen, K. M., Melbye, M. & Brandslund, I. New national Biobank of The Danish Center for Strategic Research on Type 2 Diabetes (DD2). Clin. Epidemiol. 4, 37–42 (2012).
122. Krokstad, S. et al. Cohort profile: the HUNT study, Norway. Int. J. Epidemiol. 42, 968–977 (2013).
123. Leitsalu, L. et al. Cohort profile: Estonian Biobank of the Estonian Genome Center, University of Tartu. Int. J. Epidemiol. 44, 1137–1147 (2015).
124. Al Kuwari, H. et al. The Qatar Biobank: background and methods. BMC Public Health 15, 1208 (2015).
125. Jiang, C. Q. et al. An overview of the Guangzhou biobank cohort study-cardiovascular disease subcohort (GBCS-CVD): a platform for multidisciplinary collaboration. J. Hum. Hypertens. 24, 139–150 (2010).
126. Nagai, A. et al. Overview of the BioBank Japan Project: study design and profile. J. Epidemiol. 27, S2–S8 (2017).
127. Lee, J.-E. et al. National Biobank of Korea: quality control programs of collected-human biospecimens. Osong Public Health Res. Perspect. 3, 185–189 (2012).
128. Lin, J.-C., Fan, C.-T., Liao, C.-C. & Chen, Y.-S. Taiwan Biobank: making cross-database convergence possible in the Big Data era. Gigascience 7, 1–4 (2018).