Big data hurdles in precision medicine and precision public health
Open accessAbstract
BACKGROUND: Nowadays, trendy research in biomedical sciences juxtaposes the term 'precision' to medicine and public health with companion words like big data, data science, and deep learning. Technological advancements permit the collection and merging of large heterogeneous datasets from different sources, from genome sequences to social media posts or from electronic health records to wearables. Additionally, complex algorithms supported by high-performance computing allow one to transform these large datasets into knowledge. Despite such progress, many barriers still exist against achieving precision medicine and precision public health interventions for the benefit of the individual and the population. MAIN BODY: The present work focuses on analyzing both the technical and societal hurdles related to the development of prediction models of health risks, diagnoses and outcomes from integrated biomedical databases. Methodological challenges that need to be addressed include improving semantics of study designs: medical record data are inherently biased, and even the most advanced deep learning's denoising autoencoders cannot overcome the bias if not handled a priori by design. Societal challenges to face include evaluation of ethically actionable risk factors at the individual and population level; for instance, usage of gender, race, or ethnicity as risk modifiers, not as biological variables, could be replaced by modifiable environmental proxies such as lifestyle and dietary habits, household income, or access to educational resources. CONCLUSIONS: Data science for precision medicine and public health warrants an informatics-oriented formalization of the study design and interoperability throughout all levels of the knowledge inference process, from the research semantics, to model development, and ultimately to implementation.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Despite methodological, societal, and ethical concerns, big data methods are being broadly adopted in healthcare systems for evidence-based clinical decision-making.
“Despite methodological, societal, and ethical concerns (12), big data methods are being broadly adopted in healthcare systems for evidence-based clinical decision-making.”
From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [12]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Finding the missing heritability of complex diseases
Manolio, T. A., Collins, F. S., Cox, N. J., Goldstein, D. B., Hindorff, L. A., Hunter, D. J., McCarthy, M. I., Ramos, E. M., Cardon, L. R., Chakravarti, A., et al. (2009) · Nature · doi:10.1038/nature08494 · Cited by 8,336 · see citing works
Davey Smith, G., & Ebrahim, S. (2003) · International Journal of Epidemiology · doi:10.1093/ije/dyg070 · Cited by 5,882 · see citing works
The Algorithmic Foundations of Differential Privacy
Dwork, C., & Roth, A. (2013) · doi:10.1561/9781601988195 · Cited by 3,856 · see citing works
Bias in random forest variable importance measures: Illustrations, sources and a solution
Strobl, C., Boulesteix, A., Zeileis, A., & Hothorn, T. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-25 · Cited by 3,755 · see citing works
On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
Vapnik, V. N., & Chervonenkis, A. Y. (2015) · Springer eBooks · doi:10.1007/978-3-319-21852-6_3 · Cited by 3,176 · see citing works
Multi-omics approaches to disease
Hasin, Y., Seldin, M., & Lusis, A. (2017) · Genome Biology · doi:10.1186/s13059-017-1215-1 · Cited by 2,953 · see citing works
Scalable and accurate deep learning with electronic health records
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0029-1 · Cited by 2,648 · see citing works
The Parable of Google Flu: Traps in Big Data Analysis
Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014) · Science · doi:10.1126/science.1248506 · Cited by 2,523 · cited 2× · see citing works
“However, research using non-traditional health-related data from these domains have been conducted with some success as well as with some controversy [46, 47].”
BackgroundScalable and accurate deep learning with electronic health records.
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · eScholarship (California Digital Library) · Cited by 2,242 · see citing works
Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016) · Scientific Reports · doi:10.1038/srep26094 · Cited by 1,710 · see citing works
Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2767063 · Cited by 1,688 · see citing works
van der Laan, M. J., Polley, E., & Hubbard, A. (2007) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1309 · Cited by 1,472 · see citing works
The Fourth Paradigm: Data-Intensive Scientific Discovery
Buchan, I. E., Winn, J. M., & Bishop, C. (2009) · Cited by 1,428 · see citing works
Eysenbach, G. (2009) · Journal of Medical Internet Research · doi:10.2196/jmir.1157 · Cited by 1,351 · see citing works
Personalized medicine: Time for one-person trials
Schork, N. J. (2015) · Nature · doi:10.1038/520609a · Cited by 1,235 · see citing works
Olden, J. D., & Jackson, D. A. (2002) · Ecological Modelling · doi:10.1016/s0304-3800(02)00064-9 · Cited by 1,224 · see citing works
Precision Medicine — Personalized, Problematic, and Promising
Jameson, J. L., & Longo, D. L. (2015) · New England Journal of Medicine · doi:10.1056/nejmsb1503104 · Cited by 1,116 · see citing works
George, H., Duke Jon, D., Shah Nigam, H., Reich Christian, G., Vojtech, H., Schuemie Martijn, J., Suchard Marc, A., Woong, P. R., Kei, W. I. C., Rijnbeek Peter, R., et al. (2015) · Studies in Health Technology and Informatics · doi:10.3233/978-1-61499-564-7-574 · Cited by 1,107 · see citing works
We can do better―improving the health of the American people
Schroeder, S. A. (2007) · New England Journal of Medicine · doi:10.1056/nejmsa073350 · Cited by 1,065 · see citing works
Noy, N. F. (2004) · ACM SIGMOD Record · doi:10.1145/1041410.1041421 · Cited by 1,028 · see citing works
Microbiome-wide association studies link dynamic microbial consortia to disease
Gilbert, J. A., Quinn, R. A., Debelius, J. W., Xu, Z. Z., Morton, J. T., Garg, N., Jansson, J. K., Dorrestein, P. C., & Knight, R. D. (2016) · Nature · doi:10.1038/nature18850 · Cited by 714 · see citing works
More Is Better: Recent Progress in Multi-Omics Data Integration Methods
Huang, S., Chaudhary, K., & Garmire, L. (2017) · Frontiers in Genetics · doi:10.3389/fgene.2017.00084 · Cited by 637 · see citing works
An Environment-Wide Association Study (EWAS) on Type 2 Diabetes Mellitus
Patel, C. J., Bhattacharya, J., & Butte, A. J. (2010) · PLoS ONE · doi:10.1371/journal.pone.0010746 · Cited by 581 · see citing works
(2016) · PubMed · Cited by 576 · see citing works
Validation of a common data model for active safety surveillance research
Overhage, J. M., Ryan, P. B., Reich, C. G., Hartzema, A. G., & Stang, P. E. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000376 · Cited by 548 · see citing works
Instagram photos reveal predictive markers of depression
Reece, A. G., & Danforth, C. M. (2017) · EPJ Data Science · doi:10.1140/epjds/s13688-017-0110-z · Cited by 519 · see citing works
Butler, D. (2013) · Nature · doi:10.1038/494155a · Cited by 517 · see citing works
Collins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · Annals of Internal Medicine · doi:10.7326/l15-5093-2 · Cited by 493 · see citing works
Women’s involvement in clinical trials: historical perspective and future implications
Liu, K. A., & Mager, N. A. D. (2016) · Pharmacy Practice · doi:10.18549/pharmpract.2016.01.708 · Cited by 478 · see citing works
Deep phenotyping for precision medicine
Robinson, P. N. (2012) · Human Mutation · doi:10.1002/humu.22080 · Cited by 471 · see citing works
Methods for the integration of multi-omics data: mathematical aspects
Bersanelli, M., Mosca, E., Remondini, D., Giampieri, E., Sala, C., Castellani, G., & Milanesi, L. (2016) · BMC Bioinformatics · doi:10.1186/s12859-015-0857-9 · Cited by 449 · see citing works
Why ethnic minority groups are under-represented in clinical trials: A review of the literature
Hussain-Gambles, M., Atkin, K., & Leese, B. (2004) · Health and Social Care in the Community · doi:10.1111/j.1365-2524.2004.00507.x · Cited by 447 · see citing works
Jain, S. H., Powers, B. W., Hawkins, J. B., & Brownstein, J. S. (2015) · Nature Biotechnology · doi:10.1038/nbt.3223 · Cited by 425 · see citing works
Inequalities in Life Expectancy Among US Counties, 1980 to 2014
Dwyer‐Lindgren, L., Bertozzi-Villa, A., Stubbs, R. W., Morozoff, C., Mackenbach, J. P., van Lenthe, F. J., Mokdad, A. H., & Murray, C. J. L. (2017) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2017.0918 · Cited by 418 · see citing works
Interacting with Predictions: Visual Inspection of Black-box Machine Learning Models
Krause, J., Perer, A., & Ng, K. (2016) · Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858529 · Cited by 390 · see citing works
ResearchMatch: A National Registry to Recruit Volunteers for Clinical Research
Harris, P. A., Scott, K. W., Lebo, L. A., Hassan, N. N., Lightner, C., & Pulley, J. M. (2011) · Academic Medicine · doi:10.1097/acm.0b013e31823ab7d2 · Cited by 388 · see citing works
Pirracchio, R., Petersen, M. L., Carone, M., Rigon, M. R., Chevret, S., & van der Laan, M. J. (2015) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(14)70239-5 · Cited by 374 · see citing works
Intussusception Risk after Rotavirus Vaccination in U.S. Infants
Yih, W. K., Lieu, T. A., Kulldorff, M., et al. (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1303164 · Cited by 317 · see citing works
Golding, N., Burstein, R., Longbottom, J., Browne, A. J., Fullman, N., Osgood-Zimmerman, A. E., Earl, L., Bhatt, S., Cameron, E., Casey, D., et al. (2017) · The Lancet · doi:10.1016/s0140-6736(17)31758-0 · Cited by 304 · see citing works
Paternoster, L., Tilling, K., & Davey Smith, G. (2017) · PLOS Genetics · doi:10.1371/journal.pgen.1006944 · Cited by 271 · see citing works
Naveed, M., Ayday, E., Clayton, E. W., Fellay, J., Gunter, C. A., Hubaux, J.-P., Malin, B. A., & Wang, X. (2015) · ACM Computing Surveys · doi:10.1145/2767007 · Cited by 261 · see citing works
Super Learning: An Application to the Prediction of HIV-1 Drug Resistance
Sinisi, S. E., Polley, E. C., Petersen, M. L., Rhee, S., & van der Laan, M. J. (2007) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1240 · Cited by 238 · see citing works
Will Precision Medicine Improve Population Health?
Khoury, M. J., & Galea, S. (2016) · JAMA · doi:10.1001/jama.2016.12260 · Cited by 226 · see citing works
Phenome-Wide Association Studies as a Tool to Advance Precision Medicine
Denny, J. C., Bastarache, L. A., & Roden, D. M. (2016) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-090314-024956 · Cited by 224 · see citing works
Risk, Race, and Recidivism: Predictive Bias and Disparate Impact
Skeem, J., & Lowenkamp, C. T. (2016) · Criminology · doi:10.1111/1745-9125.12123 · Cited by 217 · see citing works
Integrative methods for analyzing big data in precision medicine
Gligorijević, V., Malod‐Dognin, N., & Pržulj, N. (2015) · PROTEOMICS · doi:10.1002/pmic.201500396 · Cited by 216 · see citing works
Adolescents' presentation of food in social media: An explorative study
Holmberg, C., Chaplin, J. E., Hillman, T., & Berg, C. (2016) · Appetite · doi:10.1016/j.appet.2016.01.009 · Cited by 214 · see citing works
Reps, J. M., Schuemie, M. J., Suchard, M. A., Ryan, P. B., & Rijnbeek, P. R. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy032 · Cited by 205 · see citing works
Landers, R. N., Brusso, R. C., Cavanaugh, K. J., & Collmus, A. B. (2016) · Psychological Methods · doi:10.1037/met0000081 · Cited by 195 · see citing works
Globally optimal fuzzy decision trees for classification and regression
Suárez, A., & Lutsko, J. F. (1999) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.817409 · Cited by 193 · see citing works
Deep phenotyping: The details of disease
Delude, C. M. (2015) · Nature · doi:10.1038/527s14a · Cited by 191 · see citing works
From public health genomics to precision public health: a 20-year journey
Khoury, M. J., Bowen, M., Clyne, M., Dotson, W. D., GWINN, M. L., Green, R. F., Kolor, K., Rodriguez, J. L., Wulf, A., & Yu, W. (2017) · Genetics in Medicine · doi:10.1038/gim.2017.211 · Cited by 170 · see citing works
Ten things we have to do to achieve precision medicine
Kohane, I. S. (2015) · Science · doi:10.1126/science.aab1328 · Cited by 162 · see citing works
Genetic Testing For Alzheimer’s Disease And Its Impact On Insurance Purchasing Behavior
Zick, C. D., Mathews, C., Roberts, J. S., Cook-Deegan, R. M., Pokorski, R. J., & Green, R. C. (2005) · Health Affairs · doi:10.1377/hlthaff.24.2.483 · Cited by 160 · see citing works
Epigenome-Wide Association Studies (EWAS): Past, Present, and Future
Flanagan, J. M. (2014) · Methods in molecular biology · doi:10.1007/978-1-4939-1804-1_3 · Cited by 139 · see citing works
Evolving health care through personal genomics
Rehm, H. L. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2016.162 · Cited by 135 · see citing works
Using a suite of ontologies for preserving workflow-centric research objects
Belhajjame, K., Zhao, J., Garijo, D., Gamble, M., Hettne, K. M., Palma, R., Mina, E., Corcho, Ó., Gómez-Pérez, J. M., Bechhofer, S., et al. (2015) · Journal of Web Semantics · doi:10.1016/j.websem.2015.01.003 · Cited by 132 · see citing works
Using ontologies to improve semantic interoperability in health data
Liyanage, H., Krause, P., & de Lusignan, S. (2015) · Journal of Innovation in Health Informatics · doi:10.14236/jhi.v22i2.159 · Cited by 95 · see citing works
Epigenetics: The Language of the Cell?
Huang, B., Jiang, C., & Zhang, R. (2014) · Epigenomics · doi:10.2217/epi.13.72 · Cited by 92 · see citing works
Differential Privacy Preserving in Big Data Analytics for Connected Health
Lin, C., Song, Z., Song, H., Zhou, Y., Wang, Y., & Wu, G. (2016) · Journal of Medical Systems · doi:10.1007/s10916-016-0446-0 · Cited by 89 · see citing works
Moreno-Conde, A., Moner, D., Cruz, W. D. D., dos Santos, M. R., Maldonado, J. A., Robles, M., & Kalra, D. (2015) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv008 · Cited by 86 · see citing works
Precision medicine: opportunities, possibilities, and challenges for patients and providers
Adams, S., & Petersen, C. C. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv215 · Cited by 82 · see citing works
Ebadi, H., Sands, D., & Schneider, G. (2014) · ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (POPL) · doi:10.1145/2676726.2677005 · Cited by 76 · see citing works
Sparks, J. A., Iversen, M. D., Yu, Z., Triedman, N. A., Prado, M. G., Kroouze, R. M., Kalia, S. S., Atkinson, M. L., Mody, E., Helfgott, S. M., et al. (2017) · Arthritis Care & Research · doi:10.1002/acr.23411 · Cited by 76 · see citing works
Risk, Race, & Recidivism: Predictive Bias and Disparate ImpactPreprint
Skeem, J., & Lowenkamp, C. T. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2687339 · Cited by 73 · see citing works
Searching for differentially expressed gene combinations
Dettling, M., Gabrielson, E., & Parmigiani, G. (2005) · Genome biology · doi:10.1186/gb-2005-6-10-r88 · Cited by 66 · see citing works
Next‐generation technologies and data analytical approaches for epigenomics
Mensaert, K., Denil, S. L. I. J., Trooskens, G., Criekinge, W. V., Thas, O., & Meyer, T. D. (2013) · Environmental and Molecular Mutagenesis · doi:10.1002/em.21841 · Cited by 66 · see citing works
Custovic, A., Ainsworth, J., Arshad, H., Bishop, C., Buchan, I., Cullinan, P., Devereux, G., Henderson, J., Holloway, J., Roberts, G., et al. (2015) · Thorax · doi:10.1136/thoraxjnl-2015-206781 · Cited by 66 · see citing works
Turchioe, M. R., & Merrill, J. A. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy006 · Cited by 65 · see citing works
Uses and Misuses of Patient- and Neighborhood-level Social Determinants of Health Data
Gottlieb, L. M., Francis, D., & Beck, A. F. (2018) · The Permanente Journal · doi:10.7812/tpp/18-078 · Cited by 59 · see citing works
Fraccaro, P., Nicolo, M., Bonetto, M., Giacomini, M., Weller, P., Traverso, C. E., Prosperi, M., & OSullivan, D. (2015) · BMC Ophthalmology · doi:10.1186/1471-2415-15-10 · Cited by 48 · see citing works
Self-citationOntology driven semantic profiling and retrieval in medical information systems
Bhatt, M., Rahayu, W. J., Soni, S. P., et al. (2009) · Journal of Web Semantics · doi:10.1016/j.websem.2009.05.004 · Cited by 44 · see citing works
Alzheimer’s as a Systems-Level Disease Involving the Interplay of Multiple Cellular Networks
Castrillo, J. I., & Oliver, S. G. (2016) · Methods in Molecular Biology · doi:10.1007/978-1-4939-2627-5_1 · Cited by 44 · see citing works
Meniere′s disease: Still a mystery disease with difficult differential diagnosis
Vlastarakos, P. V., Vassiliou, A., Maragoudakis, P., et al. (2011) · Annals of Indian Academy of Neurology · doi:10.4103/0972-2327.78043 · Cited by 40 · see citing works
Precision Medicine, Genome Sequencing, and Improved Population Health
Feero, W. G., Wicklund, C. A. L., & Veenstra, D. L. (2018) · JAMA · doi:10.1001/jama.2018.2925 · Cited by 39 · see citing works
Ebadi, H., Sands, D., & Schneider, G. (2015) · ACM SIGPLAN Notices · doi:10.1145/2775051.2677005 · Cited by 38 · see citing works
Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135)Preprint
Wu, X., & Zhang, X. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.04135 · Cited by 36 · see citing works
Public health and precision medicine share a goal
Vaithinathan, A. G., & Asokan, V. (2016) · Journal of Evidence-Based Medicine · doi:10.1111/jebm.12239 · Cited by 35 · see citing works
Generalized modeling of empirical social‐ecological systems
Lade, S. J., & Niiranen, S. (2017) · Natural Resource Modeling · doi:10.1111/nrm.12129 · Cited by 33 · see citing works
Robkin, M., Weininger, S., Preciado, B., et al. (2015) · IEEE Symposium on Product Compliance Engineering (ISPCE) · doi:10.1109/ispce.2015.7138703 · Cited by 28 · see citing works
Cargnin, S., Jommi, C., Canonico, P. L., et al. (2014) · Pharmacogenomics · doi:10.2217/pgs.14.52 · Cited by 28 · see citing works
A comparative analysis of the density of the SNOMED CT conceptual content for semantic harmonization
He, Z., Geller, J., & Chen, Y. (2015) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2015.03.002 · Cited by 28 · see citing works
Reclassification of genetic-based risk predictions as GWAS data accumulate
Krier, J. B., Barfield, R., Green, R. C., et al. (2016) · Genome Medicine · doi:10.1186/s13073-016-0272-5 · Cited by 27 · see citing works
Ned, R. M. (2010) · PLoS Currents · doi:10.1371/currents.rrn1180 · Cited by 23 · see citing works
Is Precision Medicine the Future of Healthcare?
Barker, R. (2017) · Personalized Medicine · doi:10.2217/pme-2017-0060 · Cited by 23 · see citing works
Asthma Phenotypes and Endotypes: Implications for Personalised Therapy
Dean, K., & Niven, R. M. (2017) · BioDrugs · doi:10.1007/s40259-017-0242-5 · Cited by 22 · see citing works
Kenneth J. Rothman and multicausality in epidemiology
Bizouarn, P. (2012) · Revue d Épidémiologie et de Santé Publique · doi:10.1016/j.respe.2011.08.067 · Cited by 20 · see citing works
Designing a digital patient avatar in the context of the MyHealthAvatar project initiative
Maniadi, E., Kondylakis, H., Spanakis, E. G., et al. (2013) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2013.6701560 · Cited by 19 · see citing works
Health Avatar: An Informatics Platform for Personal and Private Big Data
Kim, J. H. (2014) · Healthcare Informatics Research · doi:10.4258/hir.2014.20.1.1 · Cited by 18 · see citing works
Schierup, M. H., Mailund, T., Li, H., Wang, J., Tjønneland, A. M., Vogel, U. B., Bolund, L., & Nexø, B. A. (2009) · BMC Medical Genetics · doi:10.1186/1471-2350-10-20 · Cited by 17 · see citing works
Semantic interoperability in healthcare.
Arvanitis, T. N. (2014) · PubMed · Cited by 16 · see citing works
Integrated analyses of gene expression and genetic association studies in a founder population
Cusanovich, D. A., Çalışkan, M., Billstrand, C., Michelini, K., Chavarria, C. I., Leon, S. D., Mitrano, A., Lewellyn, N., Elias, J. A., Chupp, G. L., et al. (2016) · Human Molecular Genetics · doi:10.1093/hmg/ddw061 · Cited by 16 · see citing works
Semantic Interoperability in Clinical Decision Support Systems: A Systematic Review
Luis, M.-R., & Gustav, B. J. (2015) · Studies in health technology and informatics · doi:10.3233/978-1-61499-564-7-958 · Cited by 14 · see citing works
Deeper, longer phenotyping to accelerate the discovery of the genetic architectures of diseases
Kohane, I. S. (2014) · Genome biology · doi:10.1186/gb4175 · Cited by 13 · see citing works
Matney, S. A. (2016) · Nursing · doi:10.1097/01.nurse.0000490225.92179.69 · Cited by 13 · see citing works
Semantic Integration of Patient Data and Quality Indicators Based on openEHR Archetypes
Dentler, K., ten Teije, A., Cornet, R., et al. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-36438-9_6 · Cited by 12 · see citing works
Revealing cancer subtypes with higher-order correlations applied to imaging and omics data
Graim, K. S., Liu, T. T., Achrol, A. S., Paull, E., Newton, Y., Chang, S. D., Harsh, G. R., Cordero, S. P., Rubin, D. L., & Stuart, J. M. (2017) · BMC Medical Genomics · doi:10.1186/s12920-017-0256-3 · Cited by 11 · see citing works
Tesauros y ontologías en sistemas de información documental
Codina, L., & Pedraza-Jiménez, R. (2011) · El Profesional de la Informacion · doi:10.3145/epi.2011.sep.10 · Cited by 10 · see citing works
de Vlaming, R., Okbay, A., Rietveld, C. A., Johannesson, M., Magnusson, P. K. E., Uitterlinden, A. G., van Rooij, F. J. A., Hofman, A., Groenen, P. J. F., Thurik, A. R., et al. (2017) · PLOS Genetics · doi:10.1371/journal.pgen.1006495 · Cited by 8 · see citing works
Internet of Things & Personalized Healthcare.
Sieverink, F., Siemons, L., Braakman‐Jansen, A., et al. (2016) · PubMed · Cited by 6 · see citing works
Editorial overview: Molecular and genetic bases of disease: Enter the post-GWAS era
Arking, D. E., & Rommens, J. M. (2015) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2015.10.001 · Cited by 5 · see citing works
Differential Privacy and Security
Gruska, D. P. (2016) · Fundamenta Informaticae · doi:10.3233/fi-2016-1304 · Cited by 5 · see citing works
Participation: Power to the patients
Bourzac, K. (2016) · Nature · doi:10.1038/537s66a · Cited by 4 · see citing works
Risk and the Internet of Things: Damocles, Pythia, or Pandora?
Chris, S. (2016) · Studies in health technology and informatics · doi:10.3233/978-1-61499-633-0-31 · Cited by 2 · see citing works
Amazon's cloud on the healthcare horizon
(2018) · Nature Biotechnology · doi:10.1038/nbt.4107 · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Building a knowledge graph to enable precision medicine
Chandak, P., Huang, K., & Zitnik, M. (2023) · Scientific Data · doi:10.1038/s41597-023-01960-3 · Cited by 522 · see citing works
Accessing Artificial Intelligence for Clinical Decision-Making
Giordano, C., Brennan, M., Mohamed, B., Rashidi, P., Modave, F., & Tighe, P. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.645232 · Cited by 326 · see citing works
Self-citationThe quality of social determinants data in the electronic health record: a systematic review
Cook, L. A., Sachs, J., & Weiskopf, N. G. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab199 · Cited by 153 · see citing works
Hossain, M. M., Tasnim, S., Sharma, R., Sultana, A., Shaik, A. F., Faizah, F., Kaur, R., Uppuluri, M., Sribhashyam, M., & Bhattacharya, S. (2019) · DIGITAL HEALTH · doi:10.1177/2055207619896153 · Cited by 57 · see citing works
Ethical Issues Raised by Artificial Intelligence and Big Data in Population Health: A Scoping Review
Couture, V., Roy, M.-C., Dez, E., Tremblay, F., & Bélisle-Pipon, J.-C. (2025) · Frontiers in Sociology · doi:10.3389/fsoc.2025.1536389 · Cited by 3 · see citing works
- Authors
- Prosperi, M., Min, J. S., Bian, J., & Modave, F.
- Affiliations
- University of Florida Health (US)
- Corresponding author
- Prosperi, Mattia
- Venue
- BMC Medical Informatics and Decision Making
- Published
- 2018-12
- Volume
- 18
- Issue
- 1
- Pages
- 139-139
- ISSN
- 1472-6947 (ISSN-L)
- PMC
- PMC6311005
- OpenAlex
- W2906694199
- Cited by
- 200
- References
- 105
- Citations by year
- 2026: 9 · 2025: 30 · 2024: 28 · 2023: 31 · 2022: 23 · 2021: 34 · 2020: 33 · 2019: 12
- FWCI
- 11.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Medical Informatics and Decision Making (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- University of Florida Digital Collections (University of Florida) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Center for Advancing Translational Sciences (UL1TR001427)
- Florida Department of Health (4KB16)
- Patient-Centered Outcomes Research Institute (CDRN-1501- 26692)
- Horizon 2020 Framework Programme (H2020-PHC-2014 #634650)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- precision medicine, precision public health, big data, electronic health records, data bias, denoising autoencoder, modifiable risk factors, interoperability, data science
- MeSH terms
- Big Data, Algorithms, Delivery of Health Care, Humans, Public Health, Databases, Factual, Precision Medicine, Electronic Health Records, Social Media
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
