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Realistic Synthetic Data Generation: The ATEN Framework

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Biomedical Engineering Systems and Technologies (BIOSTEC 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1024))

Abstract

Getting access to real medical data for research is notoriously difficult. Even when data exist they are usually incomplete and subject to restrictions due to confidentiality and privacy. Synthetic data (SD) are best replacements for real data but must be verifiably realistic. There is little or no investigation into systematically achieving realism in SD. This work investigates this problem, and contributes the ATEN framework, which incorporates three component approaches: (1) THOTH for synthetic data generation (SDG); (2) RA for characterising realism is SD, and (3) HORUS for validating realism in SD. The framework is found promising after its use in generating the realistic synthetic EHR (RS-EHR) for labour and birth. This framework is significant in guaranteeing realism in SDG projects. Future efforts focus on further validation of ATEN in a controlled multi-stream SDG process.

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Authors and Affiliations

  1. Queen Mary, University of London, London, UK

    Scott McLachlan & Norman Fenton

  2. Massey University, Palmerston North, New Zealand

    Kudakwashe Dube

  3. Missoula College, University of Montana, Missoula, MT, USA

    Thomas Gallagher

  4. NSW Health, Sydney, NSW, Australia

    Jennifer A. Simmonds

Authors
  1. Scott McLachlan
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  2. Kudakwashe Dube
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  3. Thomas Gallagher
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  4. Jennifer A. Simmonds
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  5. Norman Fenton
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Corresponding author

Correspondence to Scott McLachlan .

Editor information

Editors and Affiliations

  1. University of Sao Paulo, São Paulo, Brazil

    Alberto Cliquet Jr.

  2. University of Saskatchewan, Saskatoon, Canada

    Sheldon Wiebe

  3. College of Charleston, Charleston, SC, USA

    Paul Anderson

  4. University of Rome Tor Vergata, Rome, Italy

    Giovanni Saggio

  5. Aberystwyth University, Aberystwyth, UK

    Reyer Zwiggelaar

  6. LIBPhys, New University of Lisbon, Lisbon, Portugal

    Hugo Gamboa

  7. Instituto de Telecomunicações and Instituto Superior Técnico, Lisbon, Portugal

    Ana Fred

  8. Madeira Interactive Technologies Institute, Universidade da Madeira, Funchal, Portugal

    Sergi Bermúdez i Badia

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McLachlan, S., Dube, K., Gallagher, T., Simmonds, J.A., Fenton, N. (2019). Realistic Synthetic Data Generation: The ATEN Framework. In: Cliquet Jr., A., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2018. Communications in Computer and Information Science, vol 1024. Springer, Cham. https://doi.org/10.1007/978-3-030-29196-9_25

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