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Dr Leo Huberts

Dr Leo Huberts

Research Fellow
Medicine & Health
Centre for Big Data Research in Health

In 2022 Leo joined the Centre for Big Data Research in Health at the University of New South Wales as a research fellow, continuing his work on applied statistics and machine learning in healthcare. He aims to do meaningful work in bridging the gap between theory and practice.ÌýFor more information on current activities and publications visit his LinkedIn or Researchgate.

Leo C.E. Huberts (1991) holds MSc and BSc degrees in Econometrics and a BSc in Natural and Social Sciences from the University of Amsterdam (UvA). During his studies, he worked at the financial department of the Stichting CPNB, was a strategy consultant for the Kleine Consultant Amsterdam (doing pro bono strategy projects for small and medium-sized companies), served on various committees, and was board member of the Analytics Academy (running pro bono data science projects for cultural and social institutions).Ìý

During his master, he was a manager for the Big Data Alliance and founded Delph, a data science consultancy and development firm with clients ranging from political parties to municipalities and construction companies. After two years with Delph, he decided to pursue a Ph.D. degree at the University of Amsterdam. His Ph.D. on statistical and predictive process monitoring finished in 2021, after which he became Assistant Professor in Business Analytics at the University of Amsterdam until joining UNSW in November 2022.Ìý

Ìý
  • Journal articles | 2024
    Huberts LCE; Li S; Blake V; Jorm L; Yu J; Ooi SY; Gallego B, 2024, 'Predictive analytics for cardiovascular patient readmission and mortality: An explainable approach', Computers in Biology and Medicine, 174, pp. 108321,
    Journal articles | 2022
    Huberts LCE; Does RJMM; Ravesteijn B; Lokkerbol J, 2022, 'Predictive monitoring using machine learning algorithms and a real-life example on schizophrenia', Quality and Reliability Engineering International, 38, pp. 1302 - 1317,
  • Preprints | 2024
    Greenberg JD; Huberts LCE; Ritchie A; Ooi S-Y; Flynn GM; Hart GK; Gallego B, 2024, Improved Sensitivity For Detection Of Clinical Deterioration When Diagnostic Pathology And Patient Trends Are Included In Machine Learning Models,

Published studies:

  • Huberts, L.C.E., Does, R. J. M. M., Ravesteijn, B., & Lokkerbol, J. (2021b). Predictive monitoring

    using machine learning algorithms and a real-life example on schizophrenia [accepted for

    publication]. Quality and Reliability Engineering International

  • Huberts, L.C.E., Goedhart, R., & Does, R.J.M.M. (2021a). Improved control chart performance

    using cautious parameter learning [submitted for publication]. Computers & Industrial

    Engineering

  • Huberts, L.C.E., Schoonhoven, M. & Does, R.J.M.M (2020). Monitoring student progress: A case

    study to predict student success or failure. Early view in the Journal of Quality Technology

  • Huberts, L.C.E., Schoonhoven, M., & Does, R.J.M.M. (2019). The effect of continuously updating

    control chart limits on control chart performance. Quality and Reliability Engineering

    International, 35(4), 1117-1128

  • Huberts, L.C.E, Schoonhoven, M., Goedhart, R., Diko, M. D., & Does, R.J.M.M. (2018). The

    performance of control charts for large non-normally distributed datasets. Quality and Reliability Engineering International, 34(6), 979-996

  • Bun, M.J.G., & Huberts, L.C.E. (2018). The impact of higher fixed pay and lower bonuses on productivity. Journal of Labor Research, 39(1), 1-21