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Automated interpretable computational bilogy in the clinic: a framework to predicst disease severity and stratify patients from clinical data

Author

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  • Soumya Banerjee

    (University of Oxford, Oxford, United Kingdom
    Ronin Institute, Montclair, United States of America
    Complex Biological Systems Alliance, North Andover, United States of America)

Abstract

We outline an automated computational and machine learning framework that predicts disease severity and stratifies patients. We apply our framework to available clinical data. Our algorithm automatically generates insights and predicts disease severity with minimal operator intervention. The computational framework presented here can be used to stratify patients, predict disease severity and propose novel biomarkers for disease. Insights from machine learning algorithms coupled with clinical data may help guide therapy, personalize treatment and help clinicians understand the change in disease over time. Computational techniques like these can be used in translational medicine in close collaboration with clinicians and healthcare providers. Our models are also interpretable, allowing clinicians with minimal machine learning experience to engage in model building. This work is a step towards automated machine learning in the clinic.

Suggested Citation

  • Soumya Banerjee, 2017. "Automated interpretable computational bilogy in the clinic: a framework to predicst disease severity and stratify patients from clinical data," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 15(3), pages 199-208.
  • Handle: RePEc:zna:indecs:v:15:y:2017:i:3:p:199-208
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    References listed on IDEAS

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    1. Soumya Banerjee & Pascal Van Hentenryck & Manuel Cebrian, 2015. "Competitive dynamics between criminals and law enforcement explains the super-linear scaling of crime in cities," Palgrave Communications, Palgrave Macmillan, vol. 1(palcomms2), pages 15022-15022, September.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    disease severity prediction; machine learning; computational technique; big data;
    All these keywords.

    JEL classification:

    • I19 - Health, Education, and Welfare - - Health - - - Other
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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