@article{saez2018kinematics,
  title={Kinematics of Big Biomedical Data to characterize temporal variability and seasonality of data repositories: Functional Data Analysis of data temporal evolution over non-parametric statistical manifolds},
  author={S{\'a}ez, Carlos and Garc{\'\i}a-G{\'o}mez, Juan M},
  journal={International journal of medical informatics},
  volume={119},
  pages={109--124},
  year={2018},
  publisher={Elsevier}
}

@article{moreno2012unifying,
  title={A unifying view on dataset shift in classification},
  author={Moreno-Torres, Jose G and Raeder, Troy and Alaiz-Rodr{\'\i}guez, Roc{\'\i}o and Chawla, Nitesh V and Herrera, Francisco},
  journal={Pattern Recognition},
  volume={45},
  number={1},
  pages={521--530},
  year={2012},
  publisher={Elsevier}
}

@book{quionero2009dataset,
  title={Dataset shift in machine learning},
  author={Qui\~{n}onero-Candela, Joaqu{\'\i}n and Sugiyama, Masashi and Schwaighofer, Anton and Lawrence, Neil D},
  year={2009},
  publisher={The MIT Press}
}

@article{denny2013systematic,
  title={Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data},
  author={Denny, Joshua C and Bastarache, Lisa and Ritchie, Marylyn D and Carroll, Robert J and Zink, Raquel and Mosley, Jonathan D and Field, Julie R and Pulley, Jill M and Ramirez, Andrea H and Bowton, Erica and others},
  journal={Nature biotechnology},
  volume={31},
  number={12},
  pages={1102},
  year={2013},
  publisher={Nature Publishing Group}
}

@InProceedings{saez2013,
  Title                    = {Comparative study of probability distribution distances to define a metric for the stability of multi-source biomedical research data},
  Author                   = {Carlos S{\'a}ez and Montserat Robles and Juan Miguel Garc\'{i}a-G\'{o}mez},
  Booktitle                = {Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
  Year                     = {2013},
  Month                    = {July},
  Pages                    = {3226--3229},
  Location                 = {Osaka, Japan}
}

@article{saez_probabilistic_2015,
title={Probabilistic change detection and visualization methods for the assessment of temporal stability in biomedical data quality},
author={S{\'a}ez, Carlos and Rodrigues, Pedro Pereira and Gama, Jo{\~a}o and Robles, Montserrat and Garc{\'\i}a-G{\'o}mez, Juan M},
journal={Data Mining and Knowledge Discovery},
volume={29},
number={4},
pages={950--975},
year={2015},
publisher={Springer}
}

@article{saez2016applying,
	title={Applying probabilistic temporal and multisite data quality control methods to a public health mortality registry in Spain: a systematic approach to quality control of repositories},
	author={S{\'a}ez, Carlos and Zurriaga, Oscar and P{\'e}rez-Panad{\'e}s, Jordi and Melchor, Inma and Robles, Montserrat and Garc{\'\i}a-G{\'o}mez, Juan M},
	journal={Journal of the American Medical Informatics Association},
	volume={23},
	number={6},
	pages={1085--1095},
	year={2016},
	publisher={Oxford University Press}
}


@ARTICLE{Gewin2016,
	title     = "Data sharing: An open mind on open data",
	author    = "Gewin, Virginia",
	abstract  = "The move to make scientific findings transparent can be a major
	boon to research, but it can be tricky to embrace the change.",
	journal   = "Nature",
	publisher = "Nature Research",
	volume    =  529,
	number    =  7584,
	pages     = "117--119",
	month     =  jan,
	year      =  2016,
	language  = "en"
}

@ARTICLE{Andreu-Perez2015, 
	author={J. Andreu-Perez and C. C. Y. Poon and R. D. Merrifield and S. T. C. Wong and G. Z. Yang}, 
	journal={IEEE Journal of Biomedical and Health Informatics}, 
	title={Big Data for Health}, 
	year={2015}, 
	volume={19}, 
	number={4}, 
	pages={1193-1208}, 
	keywords={biomedical imaging;data privacy;diseases;genomics;medical information systems;patient treatment;proteomics;security of data;big data;biomedical informatics;clinical diagnosis;data ownership;data stewardship;disease management;genomics;governance;health informatics;imaging informatics;long-term continuous physiological sensing;metabolomics;personalized information;personalized treatment;privacy;proteomics;security;sensor informatics;translational bioinformatics;Big data;Bioinformatics;Biomedical imaging;Diseases;Drugs;Genomics;Informatics;Big data;big data;bioinformatics;health informatics;medical imaging;medical informatics;precision medicine;sensor informatics;social health;Computational Biology;Databases, Factual;Diagnostic Imaging;Humans;Medical Informatics}, 
	ISSN={2168-2194}, 
	month={July}
	}
	
	@article{schlegel2017secondary,
	title={Secondary Use of Patient Data: Review of the Literature Published in 2016},
	author={Schlegel, DR and Ficheur, G},
	journal={Yearbook of medical informatics},
	volume={26},
	number={01},
	pages={68--70},
	year={2017},
	publisher={Georg Thieme Verlag KG}
}

@article{saez2020,
    author = {Sáez, Carlos and Gutiérrez-Sacristán, Alba and Kohane, Isaac and García-Gómez, Juan M and Avillach, Paul},
    title = "{EHRtemporalVariability: delineating temporal data-set shifts in electronic health records}",
    journal = {GigaScience},
    volume = {9},
    number = {8},
    year = {2020},
    month = {07},
    abstract = "{Temporal variability in health-care processes or protocols is intrinsic to medicine. Such variability can potentially introduce dataset shifts, a data quality issue when reusing electronic health records (EHRs) for secondary purposes. Temporal data-set shifts can present as trends, as well as abrupt or seasonal changes in the statistical distributions of data over time. The latter are particularly complicated to address in multimodal and highly coded data. These changes, if not delineated, can harm population and data-driven research, such as machine learning. Given that biomedical research repositories are increasingly being populated with large sets of historical data from EHRs, there is a need for specific software methods to help delineate temporal data-set shifts to ensure reliable data reuse.EHRtemporalVariability is an open-source R package and Shiny app designed to explore and identify temporal data-set shifts. EHRtemporalVariability estimates the statistical distributions of coded and numerical data over time; projects their temporal evolution through non-parametric information geometric temporal plots; and enables the exploration of changes in variables through data temporal heat maps. We demonstrate the capability of EHRtemporalVariability to delineate data-set shifts in three impact case studies, one of which is available for reproducibility.EHRtemporalVariability enables the exploration and identification of data-set shifts, contributing to the broad examination and repurposing of large, longitudinal data sets. Our goal is to help ensure reliable data reuse for a wide range of biomedical data users. EHRtemporalVariability is designed for technical users who are programmatically utilizing the R package, as well as users who are not familiar with programming via the Shiny user interface.Availability: https://github.com/hms-dbmi/EHRtemporalVariability/Reproducible vignette: https://cran.r-project.org/web/packages/EHRtemporalVariability/vignettes/EHRtemporalVariability.htmlOnline demo: http://ehrtemporalvariability.upv.es/}",
    issn = {2047-217X},
    doi = {10.1093/gigascience/giaa079},
    url = {https://doi.org/10.1093/gigascience/giaa079},
    note = {giaa079},
    eprint = {https://academic.oup.com/gigascience/article-pdf/9/8/giaa079/33549145/giaa079.pdf},
}

