Big data hurdles in precision medicine and precision public health
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Jiang Bian | François Modave | Mattia Prosperi | Jae S. Min | François Modave | M. Prosperi | J. Bian | Jae Min
[1] David Sands,et al. Differential Privacy , 2015, POPL.
[2] Floor Sieverink,et al. Internet of Things & Personalized Healthcare. , 2016, Studies in health technology and informatics.
[3] Chris Showell,et al. Risk and the Internet of Things: Damocles, Pythia, or Pandora? , 2016, Studies in health technology and informatics.
[4] Dipak Kalra,et al. Clinical information modeling processes for semantic interoperability of electronic health records: systematic review and inductive analysis , 2015, J. Am. Medical Informatics Assoc..
[5] Aaron Roth,et al. The Algorithmic Foundations of Differential Privacy , 2014, Found. Trends Theor. Comput. Sci..
[6] M. J. van der Laan,et al. Mortality prediction in intensive care units with the Super ICU Learner Algorithm (SICULA): a population-based study. , 2015, The Lancet. Respiratory medicine.
[7] P. Canonico,et al. Diagnostic accuracy of HLA-B*57:01 screening for the prediction of abacavir hypersensitivity and clinical utility of the test: a meta-analytic review. , 2014, Pharmacogenomics.
[8] A. Lusis,et al. Considerations for the design of omics studies , 2017 .
[9] Christopher M. Danforth,et al. Instagram photos reveal predictive markers of depression , 2016, EPJ Data Science.
[10] Simon De Lusignan,et al. Using ontologies to improve semantic interoperability in health data , 2015, BMJ Health & Care Informatics.
[11] R. Niven,et al. Asthma Phenotypes and Endotypes: Implications for Personalised Therapy , 2017, BioDrugs.
[12] Steven J. Lade,et al. Generalized modeling of empirical social‐ecological systems , 2015, 1503.02846.
[13] Isaac S Kohane,et al. Ten things we have to do to achieve precision medicine , 2015, Science.
[14] Iain Buchan,et al. The Study Team for Early Life Asthma Research (STELAR) consortium ‘Asthma e-lab’: team science bringing data, methods and investigators together , 2015, Thorax.
[15] Roberto M. Lang,et al. Integrated analyses of gene expression and genetic association studies in a founder population , 2016, Human molecular genetics.
[16] Declan Butler,et al. When Google got flu wrong , 2013, Nature.
[17] Alberto Suárez,et al. Globally Optimal Fuzzy Decision Trees for Classification and Regression , 1999, IEEE Trans. Pattern Anal. Mach. Intell..
[18] Muin J. Khoury,et al. Will Precision Medicine Improve Population Health , 2017 .
[19] W Katherine Yih,et al. Intussusception risk after rotavirus vaccination in U.S. infants. , 2014, The New England journal of medicine.
[20] Lluís Codina,et al. Tesauros y ontologías en sistemas de información documental , 2011 .
[21] Yi Wang,et al. Differential Privacy Preserving in Big Data Analytics for Connected Health , 2016, Journal of Medical Systems.
[22] R. Barker,et al. Is precision medicine the future of healthcare? , 2017, Personalized medicine.
[23] James Geller,et al. Article in Press G Model Artificial Intelligence in Medicine a Comparative Analysis of the Density of the Snomed Ct Conceptual Content for Semantic Harmonization , 2022 .
[24] R. Green,et al. Genetic testing for Alzheimer's disease and its impact on insurance purchasing behavior. , 2005, Health affairs.
[25] Susan A Matney. Semantic interoperability: The good, the bad, and the ugly. , 2016, Nursing.
[26] R. Green,et al. Disclosure of Personalized Rheumatoid Arthritis Risk Using Genetics, Biomarkers, and Lifestyle Factors to Motivate Health Behavior Improvements: A Randomized Controlled Trial , 2018, Arthritis care & research.
[27] M. J. van der Laan,et al. Statistical Applications in Genetics and Molecular Biology Super Learner , 2010 .
[28] J. Flanagan,et al. Epigenome-wide association studies (EWAS): past, present, and future. , 2015, Methods in molecular biology.
[29] Natasa Przulj,et al. Integrative methods for analyzing big data in precision medicine , 2016, Proteomics.
[30] S. Schroeder. Shattuck Lecture. We can do better--improving the health of the American people. , 2007, The New England journal of medicine.
[31] Lars Bolund,et al. Haplotype frequencies in a sub-region of chromosome 19q13.3, related to risk and prognosis of cancer, differ dramatically between ethnic groups , 2009, BMC Medical Genetics.
[32] G. Davey Smith,et al. Genetic epidemiology and Mendelian randomization for informing disease therapeutics: Conceptual and methodological challenges , 2017, bioRxiv.
[33] N. Schork. Personalized medicine: Time for one-person trials , 2015, Nature.
[34] Brian W. Powers,et al. The digital phenotype , 2015, Nature Biotechnology.
[35] Rongxin Zhang,et al. Epigenetics: the language of the cell? , 2014, Epigenomics.
[36] Muin J Khoury,et al. From public health genomics to precision public health: a 20-year journey , 2017, Genetics in Medicine.
[37] L. Gottlieb,et al. Uses and Misuses of Patient- and Neighborhood-level Social Determinants of Health Data. , 2018, The Permanente journal.
[38] V. Asokan,et al. Public health and precision medicine share a goal , 2017, Journal of evidence-based medicine.
[39] O. Thas,et al. Next‐generation technologies and data analytical approaches for epigenomics , 2014, Environmental and molecular mutagenesis.
[40] Patrick B. Ryan,et al. Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data , 2018, J. Am. Medical Informatics Assoc..
[41] Jeffrey Dean,et al. Scalable and accurate deep learning with electronic health records , 2018, npj Digital Medicine.
[42] Christopher J. L. Murray,et al. Inequalities in Life Expectancy Among US Counties, 1980 to 2014: Temporal Trends and Key Drivers , 2017, JAMA internal medicine.
[43] Patrick B. Ryan,et al. Validation of a common data model for active safety surveillance research , 2012, J. Am. Medical Informatics Assoc..
[44] Julian D. Olden,et al. Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks , 2002 .
[45] D. Roden,et al. Phenome-Wide Association Studies as a Tool to Advance Precision Medicine. , 2016, Annual review of genomics and human genetics.
[46] Daniel L. Rubin,et al. Revealing cancer subtypes with higher-order correlations applied to imaging and omics data , 2017, BMC Medical Genomics.
[47] Mark J van der Laan,et al. Super Learning: An Application to the Prediction of HIV-1 Drug Resistance , 2007, Statistical applications in genetics and molecular biology.
[48] Stephen G Oliver,et al. Alzheimer's as a Systems-Level Disease Involving the Interplay of Multiple Cellular Networks. , 2016, Methods in molecular biology.
[49] Ju Han Kim,et al. Health Avatar: An Informatics Platform for Personal and Private Big Data , 2014, Healthcare informatics research.
[50] Nicolette de Keizer,et al. Semantic Integration of Patient Data and Quality Indicators Based on openEHR Archetypes , 2012, ProHealth/KR4HC.
[51] Yu-Chuan Li,et al. Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers , 2015, MedInfo.
[52] Jacqueline Merrill,et al. Converging and diverging needs between patients and providers who are collecting and using patient-generated health data: an integrative review , 2018, J. Am. Medical Informatics Assoc..
[53] Paul A Harris,et al. ResearchMatch: A National Registry to Recruit Volunteers for Clinical Research , 2012, Academic medicine : journal of the Association of American Medical Colleges.
[54] Richard T. Barfield,et al. Reclassification of genetic-based risk predictions as GWAS data accumulate , 2016, Genome Medicine.
[55] R. Ned. Genetic Testing for CYP450 Polymorphisms to Predict Response to Clopidogrel: current evidence and test availability , 2010, PLoS currents.
[56] Achim Zeileis,et al. Bias in random forest variable importance measures: Illustrations, sources and a solution , 2007, BMC Bioinformatics.
[57] Sandy Weininger,et al. Levels of conceptual interoperability model for healthcare framework for safe medical device interoperability , 2015, 2015 IEEE Symposium on Product Compliance Engineering (ISPCE).
[58] Isaac S Kohane,et al. Deeper, longer phenotyping to accelerate the discovery of the genetic architectures of diseases , 2014, Genome Biology.
[59] Lana X. Garmire,et al. More Is Better: Recent Progress in Multi-Omics Data Integration Methods , 2017, Front. Genet..
[60] Li Li,et al. Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records , 2016, Scientific Reports.
[61] Editorial overview: Molecular and genetic bases of disease: Enter the post-GWAS era. , 2015, Current opinion in genetics & development.
[62] Nancy Fullman,et al. Mapping under-5 and neonatal mortality in Africa, 2000–15: a baseline analysis for the Sustainable Development Goals , 2017, The Lancet.
[63] Luciano Milanesi,et al. Methods for the integration of multi-omics data: mathematical aspects , 2016, BMC Bioinformatics.
[64] D. Lazer,et al. The Parable of Google Flu: Traps in Big Data Analysis , 2014, Science.
[65] Thomas Hillman,et al. Adolescents' presentation of food in social media: An explorative study , 2016, Appetite.
[66] Katherine Liu,et al. Women’s involvement in clinical trials: historical perspective and future implications , 2016, Pharmacy practice.
[67] J. Wenny Rahayu,et al. Ontology driven semantic profiling and retrieval in medical information systems , 2009, J. Web Semant..
[68] Giovanni Parmigiani,et al. Searching for differentially expressed gene combinations , 2005, Genome Biology.
[69] Theodoros N. Arvanitis. Semantic Interoperability in Healthcare , 2014, ICIMTH.
[70] Natalya F. Noy,et al. Semantic integration: a survey of ontology-based approaches , 2004, SGMD.
[71] Karl Atkin,et al. Why ethnic minority groups are under-represented in clinical trials: a review of the literature. , 2004, Health & social care in the community.
[72] S. Ebrahim,et al. 'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease? , 2003, International journal of epidemiology.
[73] Manolis Tsiknakis,et al. Designing a digital patient avatar in the context of the MyHealthAvatar project initiative , 2013, 13th IEEE International Conference on BioInformatics and BioEngineering.
[74] P. Bizouarn. Kenneth J. Rothman and multicausality in epidemiology. , 2012, Revue d'epidemiologie et de sante publique.
[75] D. Longo,et al. Precision medicine--personalized, problematic, and promising. , 2015, The New England journal of medicine.
[76] A. Hofman,et al. Meta-GWAS Accuracy and Power (MetaGAP) Calculator Shows that Hiding Heritability Is Partially Due to Imperfect Genetic Correlations across Studies , 2016, bioRxiv.
[77] Mauro Giacomini,et al. Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach , 2015, BMC Ophthalmology.
[78] Peter N. Robinson,et al. Deep phenotyping for precision medicine , 2012, Human mutation.
[79] Carole A. Goble,et al. Using a suite of ontologies for preserving workflow-centric research objects , 2015, J. Web Semant..
[80] T. Nikolopoulos,et al. Meniere’s disease: Still a mystery disease with difficult differential diagnosis , 2011, Annals of Indian Academy of Neurology.
[81] E. Cohen,et al. We Can Do Better — Improving the Health of the American People , 2008 .
[82] Vladimir Vapnik,et al. Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities , 1971 .
[83] Christopher T. Lowenkamp,et al. RISK, RACE, AND RECIDIVISM: PREDICTIVE BIAS AND DISPARATE IMPACT*: RISK, RACE, AND RECIDIVISM , 2016 .
[84] Andrew B. Collmus,et al. A primer on theory-driven web scraping: Automatic extraction of big data from the Internet for use in psychological research. , 2016, Psychological methods.
[85] Cathryn M. Delude. Deep phenotyping: The details of disease , 2015, Nature.
[86] David Veenstra,et al. Precision Medicine, Genome Sequencing, and Improved Population Health. , 2018, JAMA.
[87] Johan Gustav Bellika,et al. Semantic Interoperability in Clinical Decision Support Systems: A Systematic Review , 2015, MedInfo.
[88] H. Rehm. Evolving health care through personal genomics , 2017, Nature Reviews Genetics.
[89] James T. Morton,et al. Microbiome-wide association studies link dynamic microbial consortia to disease , 2016, Nature.
[90] Carl A. Gunter,et al. Privacy in the Genomic Era , 2014, ACM Comput. Surv..
[91] Xi Zhang,et al. Automated Inference on Criminality using Face Images , 2016, ArXiv.
[92] Kenney Ng,et al. Interacting with Predictions: Visual Inspection of Black-box Machine Learning Models , 2016, CHI.
[93] Damas P. Gruska. Differential Privacy and Security , 2014, CS&P.
[94] G. Eysenbach. Infodemiology and Infoveillance: Framework for an Emerging Set of Public Health Informatics Methods to Analyze Search, Communication and Publication Behavior on the Internet , 2009, Journal of medical Internet research.
[95] Atul J. Butte,et al. An Environment-Wide Association Study (EWAS) on Type 2 Diabetes Mellitus , 2010, PloS one.
[96] Samantha A. Adams,et al. Precision medicine: opportunities, possibilities, and challenges for patients and providers , 2016, J. Am. Medical Informatics Assoc..
[97] Iain E. Buchan,et al. A unified modeling approach to data-intensive healthcare , 2009, The Fourth Paradigm.
[98] Judy H. Cho,et al. Finding the missing heritability of complex diseases , 2009, Nature.
[99] K. Bourzac. Participation: Power to the patients , 2016, Nature.
[100] Jennifer L. Skeem,et al. Risk, Race, & Recidivism: Predictive Bias and Disparate Impact , 2016 .
[101] Parisa Rashidi,et al. Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis , 2017, IEEE Journal of Biomedical and Health Informatics.