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Self-Service Data Science for Adverse Event Prediction in Electronic Healthcare Records

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Research and Innovation Forum 2020 (RIIFORUM 2020)

Part of the book series: Springer Proceedings in Complexity ((SPCOM))

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Abstract

Healthcare is a data intensive industry in which data mining has a great potential for improving the wellbeing of patients. However, a multitude of barriers impedes the application of machine learning. This work focuses on medical adverse event prediction by domain experts. In this research we present AutoCrisp as a self-service data science prototype for multivariate sequential classification on electronic healthcare records to facilitate self-service data science by domain experts, without requiring any sophisticated data mining knowledge. We performed an empirical case study with the objective to predict bleedings with the use of AutoCrisp. Our results show that multivariate sequential classification for medical adverse event prediction can indeed be made accessible to healthcare professionals by providing appropriate tooling support.

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Author information

Authors and Affiliations

  1. Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC, Utrecht, The Netherlands

    Marco Spruit & Niels de Vries

  2. Department of Public Health and Primary Care, Leiden University Medical Centre, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands

    Marco Spruit

Authors
  1. Marco Spruit
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  2. Niels de Vries
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Correspondence to Marco Spruit .

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Editors and Affiliations

  1. Research & Innovation Institute (Rii), Warsaw, Poland

    Anna Visvizi

  2. Effat University, Jeddah, Saudi Arabia

    Miltiadis D. Lytras

  3. Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

    Naif R. Aljohani

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Spruit, M., de Vries, N. (2021). Self-Service Data Science for Adverse Event Prediction in Electronic Healthcare Records. In: Visvizi, A., Lytras, M.D., Aljohani, N.R. (eds) Research and Innovation Forum 2020. RIIFORUM 2020. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-62066-0_39

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  • DOI: https://doi.org/10.1007/978-3-030-62066-0_39

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